CN112491816A

CN112491816A - Service data processing method and device

Info

Publication number: CN112491816A
Application number: CN202011259766.3A
Authority: CN
Inventors: 赵豪; 曹世杰; 乜聚虎
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2020-11-12
Filing date: 2020-11-12
Publication date: 2021-03-12

Abstract

The embodiment of the specification provides a service data processing method and a device, wherein the service data processing method comprises the following steps: sampling service data to obtain a service field and a corresponding sample field value; determining service fields except identifiable sensitive fields in a service field set consisting of the service fields as fields to be processed; judging whether the field to be processed is a newly added sensitive field or not; if yes, marking the field to be processed with the newly added sensitive field as a judgment result to obtain a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway.

Description

Service data processing method and device

Technical Field

The present disclosure relates to the field of electronic technologies, and in particular, to a method and an apparatus for processing service data.

Background

With the development of the internet, a client of an application generates a large number of completely new business fields. Generally, the history data of the service field is summarized to obtain a sensitive field, and then desensitization treatment can be performed according to the known sensitive field, which takes a while. Unknown sensitive fields cannot be identified accurately in time, sensitive data of a user is easy to leak, and personal privacy of the user is violated.

Disclosure of Invention

One or more embodiments of the present specification provide a service data processing method. The service data processing method comprises the following steps: and sampling the service data to obtain a service field and a corresponding sample field value. And determining the service fields except the identifiable sensitive fields in the service field set consisting of the service fields as the fields to be processed. Judging whether the field to be processed is a newly added sensitive field; if yes, marking the field to be processed which is determined as the newly added sensitive field as a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway.

One or more embodiments of the present specification provide a service data processing apparatus. The service data processing device comprises: and the data sampling module is configured to sample the service data to obtain a service field and a corresponding sample field value. And the to-be-processed field determining module is configured to determine the service fields except the identifiable sensitive fields in the service field set formed by the service fields as the to-be-processed fields. And the newly added sensitive field determining module is configured to judge whether the field to be processed is a newly added sensitive field. And the field marking module is configured to mark the field to be processed which is determined as the newly added sensitive field as a marked field, and add the marked field to an identifiable sensitive field set configured by the service gateway.

One or more embodiments of the present specification provide a service data processing apparatus. The apparatus includes a memory and a processor. The memory is for storing computer-executable instructions, and the processor is for executing the computer-executable instructions to: and sampling the service data to obtain a service field and a corresponding sample field value. And determining the service fields except the identifiable sensitive fields in the service field set consisting of the service fields as the fields to be processed. Judging whether the field to be processed is a newly added sensitive field; if yes, marking the field to be processed which is determined as the newly added sensitive field as a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway.

One or more embodiments of the present specification provide a storage medium. Stored with computer instructions that, when executed by a processor, implement: and sampling the service data to obtain a service field and a corresponding sample field value. And determining the service fields except the identifiable sensitive fields in the service field set consisting of the service fields as the fields to be processed. Judging whether the field to be processed is a newly added sensitive field; if yes, marking the field to be processed which is determined as the newly added sensitive field as a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway.

Drawings

In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and that other drawings can be obtained by those skilled in the art without inventive exercise.

Fig. 1 is a first processing flow diagram of a service data processing method according to one or more embodiments of the present disclosure;

fig. 2 is a second processing flow diagram of a service data processing method according to one or more embodiments of the present disclosure;

fig. 3 is a schematic block diagram of a service data processing apparatus according to one or more embodiments of the present disclosure;

fig. 4 is a schematic structural diagram of a service data processing device according to one or more embodiments of the present specification.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.

One or more embodiments of the present specification provide a service data processing method, and fig. 1 is a first processing flow chart of a service data processing method provided in one or more embodiments of the present specification.

Referring to fig. 1, the service data processing method provided in this embodiment specifically includes steps S102 to S108 described below.

The business data processing method in the embodiment determines the information entropy of the field to be processed by using the information entropy algorithm, and then determines whether the field to be processed is a newly added sensitive field according to the size of the information entropy, so that the newly generated sensitive field can be quickly and accurately identified, the risk of sensitive data leakage of the user caused by the fact that an unknown sensitive field cannot be identified is reduced, and the privacy of the user is protected.

Step S102, sampling processing is carried out on the service data, and a service field and a corresponding sample field value are obtained.

In some embodiments, step S102 may be performed by the service gateway, the server, or other electronic devices. The service gateway is a gateway device for connecting the service network and the bearer network, and may be deployed on a server, a client, or a separate service gateway device. The service gateway is used for analyzing service data which are generated on a client of the application program and comprise sensitive data corresponding to the user.

The business data is generated by a client of the application program, and the business data includes but is not limited to sensitive data corresponding to the user, public data irrelevant to the user and non-sensitive data corresponding to the user.

Sensitive data corresponding to a user refers to private data of the user, such as an identification number of the user, a transaction account identification code of the user, and the like. The following description will be made by taking the service data in the form of fields, i.e. service fields, as an example: in some cases, for a service field of the same kind, different users have different field values corresponding to the service field, for example, the identification numbers of different users are different. In other cases, for a service field of the same category, most users have different field values corresponding to the service field, and a small number of users have the same field value corresponding to the service field.

The public data irrelevant to the user refers to business data irrelevant to sensitive data of the user and generated in a client of the application program, and it can be understood that, for the same kind of business fields, the field values of most users in the business fields are the same, for example, through an application program A which can be used for weather inquiry, a plurality of users inquire weather data in the same time period, the application program A generates weather fields relevant to the weather data, and the field values of the users in the weather fields are all sunny days.

The non-sensitive data corresponding to the user refers to the service data that does not relate to the privacy of the user although corresponding to the user, and is not described herein again.

The application program may be an application program running on a mobile terminal, such as a mobile phone and a computer, or an application program running on a device, and an Internet of Things (IoT) device, such as a smart home appliance and a wearable device. The application may be a separate application, such as a shopping application, a mapping application, or may be a sub-application running in an application.

A client of an application program generates a large amount of processed service data every day, a service gateway deployed on the client processes the service data, and then uploads the processed service data to a network, for example, a server, a cloud storage space or a block chain corresponding to the client.

Optionally, the method for processing service data is applied to a service gateway, where the service gateway is deployed on a client, and performs sampling processing on the service data to obtain a service field and a corresponding sample field value, and includes: acquiring service data generated by a client, wherein the service data comprises a service field and a corresponding field value to be sampled; and sampling the field value to be sampled according to the field value sampling rate to obtain a sample field value corresponding to the service field.

The service data generated by the client comprises a service field and a corresponding field value to be sampled, wherein the field value to be sampled refers to the field value corresponding to the service field in the service data which is not subjected to sampling processing. For example, in the service data generated by the client, the value of the field to be sampled corresponding to the service field "id number" is the id numbers of xxxxxx users.

Sampling is performed on a field value to be sampled according to a field value sampling rate to obtain a sample field value corresponding to a service field, for example, xxxxxx field values to be sampled corresponding to a service field "identification number" are sampled according to a field value sampling rate Y% to obtain XX sample field values.

By sampling the field value to be sampled, the data volume to be processed can be greatly reduced, the workload of the service gateway is reduced, and a newly added sensitive field can be found out based on the processing of a small number of sample field values in the subsequent steps so as to perform desensitization processing and upload.

And step S104, determining the service fields except the identifiable sensitive fields in the service field set consisting of the service fields as the fields to be processed.

In some embodiments, step S104 may be performed by the service gateway, the server, or other electronic devices.

The sensitive fields in this embodiment are divided into two types. One is a known sensitive field, i.e., a sensitive field can be identified. The service gateway is configured with a set of identifiable sensitive fields, and the set of identifiable sensitive fields includes at least one identifiable sensitive field. The recognizable sensitive fields can be predetermined according to laws and regulations for protecting the privacy of users, can also be predetermined according to manual setting, and can also be predetermined by carrying out induction and arrangement on historical data of business data.

The other sensitive field is an unknown sensitive field, namely a new sensitive field. Under the condition of rapid development of the mobile internet, a large number of new sensitive fields corresponding to the users are generated every day, namely, new sensitive fields are added. The determination and protection of the newly added sensitive field often lags behind the point in time of generation of the newly added sensitive field. Therefore, after the newly added sensitive field is generated, the newly added sensitive field does not exist in the recognizable sensitive field set, and the field value corresponding to the newly added sensitive field cannot be desensitized through the recognizable sensitive field in the recognizable sensitive field set to upload the desensitized field value. The newly added sensitive field is determined by step S106.

The service field set comprises at least one service field, and the service field comprises but is not limited to an identifiable sensitive field, a newly added sensitive field, a public service field and a non-sensitive field corresponding to a user.

And determining the service fields except the identifiable sensitive fields in the service field set as the fields to be processed, wherein the fields to be processed may be newly added sensitive fields, public service fields or non-sensitive fields corresponding to the users. In this embodiment, a small number of non-sensitive fields corresponding to the user in the fields to be processed are negligible, and most of the fields to be processed are one of the public service fields or the newly added sensitive fields.

Optionally, the recognizable sensitive field set contains recognizable sensitive fields; determining the service fields except the identifiable sensitive fields in the service field set composed of the service fields as the fields to be processed, including: judging whether the service fields in the service field set are identifiable sensitive fields in an identifiable sensitive field set or not; if yes, marking the service field judged as the identifiable sensitive field to obtain a second marked field, performing data desensitization on a field value corresponding to the second marked field to obtain a desensitized field value serving as a second target field value, and uploading the desensitized field value to a server; and if not, determining the service field judged as the unidentifiable sensitive field as the field to be processed.

The recognizable sensitive field set comprises at least one recognizable sensitive field, such as an identification number field and a transaction account number identification number field.

And reading each identifiable sensitive field in the identifiable sensitive field set configured by the service gateway. And judging whether the service fields in the service field set are identifiable sensitive fields. And if the service field is the recognizable sensitive field, marking the service field with the recognizable sensitive field as the judgment result to obtain a second marked field. And determining a second labeled field and a corresponding field value in the service data, carrying out data desensitization treatment on the field value corresponding to the second labeled field, obtaining the desensitized field value as a second target field value, and uploading the desensitized field value to a server. And if the service field is not the recognizable sensitive field, determining the service field with the non-recognizable sensitive field as the field to be processed.

The field value corresponding to the second tag field refers to a field value corresponding to the second tag field in the service data which is not subjected to sampling processing. The second target field value may be uploaded to a server, cloud storage space or blockchain, etc.

And step S106, judging whether the field to be processed is a newly added sensitive field.

In some embodiments, step S104 may be performed by the service gateway, the server, or other electronic devices. The definitions of the to-be-processed field and the newly added sensitive field are already stated in the description of step S104, and are not described herein again.

Optionally, whether the field to be processed is a newly added sensitive field is determined by the following method: calculating a first information entropy of the field to be processed based on the sample field value of the field to be processed; judging whether the first information entropy is larger than a first information entropy threshold value or not; and if so, determining the field to be processed with the first information entropy larger than the first information entropy threshold value as a newly increased sensitive field.

And if the first information entropy is smaller than or equal to a first information entropy threshold value, determining the field to be processed, of which the first information entropy is smaller than or equal to the first information entropy threshold value, as the non-newly-increased sensitive field. The non-newly added sensitive field may be a public service field, or may be a non-sensitive field corresponding to the user.

Calculating the first information entropy of each field to be processed by using a calculation formula of the information entropy; judging whether the first information entropy is larger than a predetermined first information entropy threshold value or not; if yes, determining the field to be processed, of which the first information entropy is larger than the first information entropy threshold value, as a newly increased sensitive field; if not, determining the field to be processed with the first information entropy smaller than or equal to the first information entropy threshold value as the non-sensitive field. The non-sensitive field comprises a public service field and a non-sensitive field corresponding to a user. In some cases, the number of non-sensitive fields corresponding to a user is less negligible.

The definition of the information entropy is: a measure of the size of the information volume, describing the uncertainty of the random variable. The larger the uncertainty of an event, the larger the information amount and the larger the information entropy. The calculation formula of the information entropy is as follows:

where H (X) is the information entropy, p (x)_i) Is an event x_iThe probability of occurrence. In this embodiment, the formula can be understood as H (X) is the first information entropy of the field to be processed, p (x)_i) For the field to be processed, the sample field value is x_iThe probability of (c).

The first information entropy in this embodiment is a predetermined information entropy used for determining whether the field to be processed is a newly added sensitive field. In general, the larger the information entropy is, the larger the information amount contained in each sample field value of the field to be processed is, the more different sample field values of the field to be processed are; the smaller the information entropy, the smaller the amount of information contained in the respective sample field value of the field to be processed, the fewer the different sample field values of the field to be processed. For example, the field a to be processed and the field b to be processed both correspond to XX sample field values, where most of the sample field values are the same for the field a to be processed, a small number of the sample field values are the same for the field b to be processed, and most of the sample field values are different, and then the information amount contained in the field a to be processed is smaller than that of the field b to be processed.

Adding a sensitive field, such as a service field corresponding to the identification number, wherein the identification numbers of different users are different; the service fields corresponding to the public service fields, such as weather data, are searched by a plurality of users for the weather data in the same time period, and the obtained weather data are the same. Therefore, the amount of information contained in the newly added sensitive field is greater than the amount of information contained in the common service field. Therefore, by setting the predetermined first information entropy threshold, the field to be processed, of which the first information entropy is larger than the first information entropy threshold, can be determined as the newly-added sensitive field, the unknown sensitive field can be rapidly and accurately identified, and the risk that the sensitive data of the user is leaked due to the fact that the newly-generated sensitive field cannot be identified is reduced.

Optionally, before the step of determining whether the first information entropy is greater than the first information entropy threshold is executed, the method further includes: determining a first service type corresponding to the service data; and determining the information entropy threshold corresponding to the first service type as a first information entropy threshold.

The service data may correspond to a plurality of service types. And configuring an information entropy threshold value for the service type according to the service characteristics of the service type. For example, the weather query service involves few sensitive data corresponding to the user, so the information entropy threshold configured for the service type of the weather query service is higher; the transaction service relates to more sensitive data corresponding to the user, so the information entropy threshold configured for the service type of the transaction service is lower than the information entropy threshold corresponding to the service type of the weather inquiry service.

A first business type corresponding to the business data is determined, e.g., the first business type corresponding to the business data generated by the client of the shopping application is determined to be a shopping business type. And determining an information entropy threshold corresponding to the first service type as the first information entropy threshold, for example, determining an information entropy threshold corresponding to the shopping service type as the first information entropy threshold.

Optionally, the service data processing method further includes: calculating a second information entropy of the field to be processed based on the sample field value of the field to be processed; judging whether the second information entropy is smaller than a second information entropy threshold value or not; and if so, determining the field to be processed with the second information entropy smaller than the second information entropy threshold value as the public service field.

Calculating second information entropy of each field to be processed by using a calculation formula of the information entropy; judging whether the second information entropy is larger than a predetermined second information entropy threshold value or not; if so, determining the field to be processed with the second information entropy smaller than the second information entropy threshold value as a public service field; if not, determining the field to be processed with the second information entropy larger than or equal to the first information entropy threshold value as a non-public service field. The non-public service field comprises a newly added sensitive field and a non-sensitive field corresponding to the user. In some cases, the number of non-sensitive fields corresponding to a user is less negligible.

where H (X) is the information entropy, p (x)_i) Is an event x_iThe probability of occurrence. In this embodiment, the formula can be understood as H (X) is the second information entropy of the field to be processed, p (x)_i) For the field to be processed, the sample field value is x_iThe probability of (c).

The second information entropy in this embodiment is a predetermined information entropy used for determining whether the field to be processed is a public service field. In general, the larger the information entropy is, the larger the information amount contained in each sample field value of the field to be processed is, the more different sample field values of the field to be processed are; the smaller the information entropy, the smaller the amount of information contained in the respective sample field value of the field to be processed, the fewer the different sample field values of the field to be processed. For example, the field a to be processed and the field b to be processed both correspond to XX sample field values, where most of the sample field values are the same for the field a to be processed, a small number of the sample field values are the same for the field b to be processed, and most of the sample field values are different, and then the information amount contained in the field a to be processed is smaller than that of the field b to be processed.

By setting a predetermined second information entropy threshold, the field to be processed, of which the second information entropy is smaller than the second information entropy threshold, can be determined as a common field, and the common service field in the service field can be identified quickly and accurately.

In one example, the number of the newly added sensitive field and the field to be processed other than the public service field is small and negligible, the first information entropy and the second information entropy are both target information entropies, and the first information entropy threshold and the second information entropy threshold are both target information entropy thresholds, so that the service gateway can determine whether the field to be processed is the newly added sensitive field or the public service field according to the comparison result only by calculating the target information entropy once according to the sample field value of the field to be processed and then comparing the target information entropy with the target information entropy threshold.

In this example, after determining the public service field in the field to be processed, the service gateway filters the public service field in the field to be processed, and only the newly added sensitive field remains.

Specifically, if the target information entropy is larger than the target information entropy threshold, determining the field to be processed, of which the target information entropy is larger than the target information entropy threshold, as a newly-added sensitive field; and if the target information entropy is smaller than the target information entropy threshold, determining the field to be processed with the target information entropy smaller than the target information entropy threshold as a public service field.

In an example, the step S106 may also be executed by the server, and after determining the new sensitive field, the server performs a marking process on the new sensitive field to obtain a third marked field, and returns the third marked field to the client that generates the service data, so that the client performs a desensitization process on the service data through the third marked field, and uploads the desensitization process to the server. Step S106 is executed by the server, which is beneficial to reduce the workload of the client, and facilitates the sharing of the newly added sensitive field determined by the server by a plurality of clients.

If the determination result in the step S106 is yes, step S108 is executed, and if the determination result in the step S106 is no, the field to be processed is skipped, and whether the next field to be processed is a newly added sensitive field is determined.

And step S108, marking the field to be processed which is judged to be the newly added sensitive field as a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway.

And marking the field to be processed with the newly added sensitive field as a judgment result to obtain a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway.

In one example, the service gateway performs a marking operation, specifically, performs marking processing on a field to be processed whose determination result is a newly added sensitive field to obtain a marked field, and performs a mark field adding operation with the service gateway, specifically, adds the marked field to an identifiable sensitive field set configured by the service gateway, and performs two operations of parallel processing for the service gateway. The marking operation can be executed first, and then the operation of adding the marking field can be executed; or the operation of adding the mark field can be executed first, and then the mark operation is executed; the marking operation and the add mark field operation may also be performed simultaneously.

Optionally, after the step of marking the field to be processed, which is determined as the newly added sensitive field, as the mark field is executed, the method further includes: determining a tag field and a corresponding field value in service data; and carrying out data desensitization treatment on the field value corresponding to the marked field to obtain a desensitized field value serving as a target field value, and uploading the desensitized field value to a server.

And determining the marked field and the corresponding field value obtained in the step S108 in the service data, performing data desensitization on the field value corresponding to the marked field, obtaining the desensitized field value as a target field value, and uploading the target field value to a server. The field value corresponding to the tag field refers to a field value corresponding to the tag field in the service data which is not subjected to sampling processing. The target field value may be uploaded to a server, cloud storage space or blockchain, etc.

The field to be processed, which is determined to be the newly added sensitive field, is marked as the marked field, and the marked field is added into the identifiable sensitive field set configured by the service gateway, so that the identifiable sensitive field set can be timely and accurately expanded, and the newly generated sensitive field is marked for desensitization processing and uploading, thereby avoiding the problem of sensitive data leakage of the user caused by the fact that the sensitive field which is not predetermined cannot be identified, and protecting the sensitive data of the user.

Optionally, performing data desensitization on a field value corresponding to the labeled field to obtain a desensitized field value as a target field value, and uploading the desensitized field value to the server, where the desensitization includes: determining a second service type corresponding to the service data; determining a target desensitization format according to the service sensitivity corresponding to the second service type; and according to the target desensitization format, carrying out data desensitization treatment on the field value corresponding to the marked field to obtain a desensitized field value serving as a target field value, and uploading the desensitized field value to a server.

The second service type corresponding to the service data, for example, the service data is generated by a client of the weather query application program, and then the second service type corresponding to the service data is the service type corresponding to the weather query service. Multiple levels of traffic sensitivity, e.g., high sensitivity, medium sensitivity, low sensitivity, are set for the traffic type. And (3) the service type corresponding to the weather inquiry service is low sensitivity, data desensitization treatment is carried out on the field value corresponding to the marked field according to a target desensitization format corresponding to the low sensitivity, for example, characters except the last three bits in the character string corresponding to the field value are reserved, for example, the last three bits of the character string corresponding to the field value are replaced by 'x', the desensitized field value is obtained and serves as a target field value, and the target field value is transmitted to the server.

To sum up, one embodiment of the method for processing service data samples service data to obtain a service field and a corresponding sample field value; determining service fields except identifiable sensitive fields in a service field set consisting of the service fields as fields to be processed; judging whether the field to be processed is a newly added sensitive field or not; if yes, the field to be processed, which is determined as the newly added sensitive field, is marked as the marked field, and the marked field is added into the identifiable sensitive field set configured by the service gateway.

Fig. 2 is a second processing flow diagram of a service data processing method according to one or more embodiments of the present disclosure. Referring to fig. 2, the service data processing method provided in this embodiment specifically includes steps S102 to S112 described below.

Acquiring service data, wherein the service data comprises a service field and a corresponding field value to be sampled; and sampling the field value to be sampled according to the field value sampling rate to obtain a sample field value corresponding to the service field. Step S102 can refer to the embodiment of fig. 1, and is not described herein again.

Judging whether the service fields in the service field set are identifiable sensitive fields in an identifiable sensitive field set or not; if yes, marking the service field judged as the identifiable sensitive field to obtain a second marked field, performing data desensitization on a field value corresponding to the second marked field to obtain a desensitized field value serving as a second target field value, and uploading the desensitized field value to a server; and if not, determining the service field judged as the unidentifiable sensitive field as the field to be processed. Step S104 can refer to the embodiment of fig. 1, and is not described herein again.

Calculating a first information entropy of the field to be processed based on the sample field value of the field to be processed; judging whether the first information entropy is larger than a first information entropy threshold value or not; and if the information entropy is not greater than the first information entropy threshold, determining the field to be processed, of which the first information entropy is less than or equal to the first information entropy threshold, as a newly increased sensitive field. The non-newly added sensitive field may be a public service field, or may be a non-sensitive field corresponding to the user. Step S106 can refer to the embodiment of fig. 1, and is not described herein again.

If the field to be processed is the newly added sensitive field, step S108 is executed. And if the field to be processed is the newly increased sensitive field, skipping processing the field to be processed, determining the next field to be processed, judging whether the next field to be processed is the newly increased sensitive field, if so, executing the step S108, otherwise, skipping processing the next field to be processed, and returning to the executing step to determine the next field to be processed.

And marking the field to be processed with the newly added sensitive field as a judgment result to obtain a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway. Step S108 can refer to the embodiment of fig. 1, and is not described herein again.

Step S110, determining a tag field and a corresponding field value in the service data.

The tag field and the corresponding field value obtained in step S108 are determined in the service data. The service data is full service data without sampling processing.

And step S112, carrying out data desensitization treatment on the field value corresponding to the marked field to obtain a desensitized field value serving as a target field value, and uploading the desensitized field value to a server.

And carrying out data desensitization treatment on the field value corresponding to the marked field to obtain a desensitized field value serving as a target field value, and uploading the desensitized field value to a server. The field value corresponding to the tag field refers to a field value corresponding to the tag field in the service data which is not subjected to sampling processing. The target field value may be uploaded to a server, cloud storage space or blockchain, etc.

In summary, the service data processing method provided in this embodiment samples the service data to obtain a service field and a corresponding sample field value; determining service fields except identifiable sensitive fields in a service field set consisting of the service fields as fields to be processed; judging whether the field to be processed is a newly added sensitive field or not; if yes, marking the field to be processed which is determined as the newly added sensitive field as a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway; determining a tag field and a corresponding field value in service data; determining a tag field and a corresponding field value in service data; the technical scheme can timely and accurately identify and mark the newly added sensitive field, reduce the risk of sensitive data leakage of a user caused by uploading the field value of the unknown sensitive field which cannot be identified to a network, and protect the privacy of the user.

On the basis of the same technical concept, corresponding to the service data processing method described in fig. 1, one or more embodiments of the present specification further provide a service data processing apparatus. Fig. 3 is a schematic block diagram of a service data processing apparatus according to one or more embodiments of the present disclosure, where as shown in fig. 3, the service data processing apparatus includes:

a data sampling module 302 configured to sample the service data to obtain a service field and a corresponding sample field value;

a to-be-processed field determining module 304, configured to determine, as to-be-processed fields, service fields other than identifiable sensitive fields in a service field set composed of the service fields;

a newly added sensitive field determining module 306 configured to determine whether the field to be processed is a newly added sensitive field;

and the field marking module 308 is configured to mark the field to be processed, which is determined as the newly added sensitive field, as a marked field, and add the marked field to the identifiable sensitive field set configured by the service gateway.

Optionally, the service data processing apparatus further includes:

a tag field determination module configured to determine a tag field and a corresponding field value in the service data;

and the marked field desensitization module is configured to perform data desensitization on a field value corresponding to the marked field, obtain the desensitized field value as a target field value and upload the desensitized field value to the server.

Optionally, whether the field to be processed is a newly added sensitive field is determined by the following method:

calculating a first information entropy of the field to be processed based on the sample field value of the field to be processed;

judging whether the first information entropy is larger than a first information entropy threshold value or not;

and if so, determining the field to be processed with the first information entropy larger than the first information entropy threshold value as a newly increased sensitive field.

Optionally, the service data processing apparatus further includes:

the second information entropy calculation module is configured to calculate second information entropy of the field to be processed based on the sample field value of the field to be processed;

the second information entropy judging module is configured to judge whether the second information entropy is smaller than a second information entropy threshold value;

and the public service field determining module is configured to determine the field to be processed with the second information entropy smaller than the second information entropy threshold value as the public service field.

Optionally, the service data processing apparatus is applied to a service gateway, the service gateway is deployed on a client, and the data sampling module 302 is specifically configured to:

acquiring service data generated by a client, wherein the service data comprises a service field and a corresponding field value to be sampled;

and sampling the field value to be sampled according to the field value sampling rate to obtain a sample field value corresponding to the service field.

Optionally, the recognizable sensitive field set contains recognizable sensitive fields; the to-be-processed field determining module 304 is specifically configured to:

judging whether the service fields in the service field set are identifiable sensitive fields in an identifiable sensitive field set or not;

if yes, marking the service field judged as the identifiable sensitive field to obtain a second marked field, performing data desensitization on a field value corresponding to the second marked field to obtain a desensitized field value serving as a second target field value, and uploading the desensitized field value to a server;

and if not, determining the service field judged as the unidentifiable sensitive field as the field to be processed.

Optionally, the service data processing apparatus further includes:

a first service type determining module configured to determine a first service type corresponding to the service data;

the first information entropy threshold value determining module is configured to determine an information entropy threshold value corresponding to the first service type as a first information entropy threshold value.

Optionally, the mark field desensitization module is specifically configured to:

determining a second service type corresponding to the service data;

determining a target desensitization format according to the service sensitivity corresponding to the second service type;

and according to the target desensitization format, carrying out data desensitization treatment on the field value corresponding to the marked field to obtain a desensitized field value serving as a target field value, and uploading the desensitized field value to a server.

It should be noted that the embodiment of the service data processing apparatus in this specification and the embodiment of the service data processing method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the foregoing corresponding service data processing method, and repeated details are not described again.

Further, corresponding to the service data processing method described above, based on the same technical concept, one or more embodiments of the present specification further provide a service data processing device, where the device is configured to execute the service data processing method described above, and fig. 4 is a schematic structural diagram of a service data processing device provided in one or more embodiments of the present specification.

As shown in fig. 3, the service data processing device may have a relatively large difference due to different configurations or performances, and may include one or more processors 401 and a memory 402, where one or more stored applications or data may be stored in the memory 402. Wherein memory 402 may be transient or persistent. The application program stored in memory 402 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a data push device. Still further, the processor 401 may be configured to communicate with the memory 402 to execute a series of computer-executable instructions in the memory 402 on a data push device. The data pushing apparatus may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input-output interfaces 405, one or more keyboards 406, and the like.

In a specific embodiment, the business data processing apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the data pushing apparatus, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:

sampling the service data to obtain a service field and a corresponding sample field value;

determining service fields except identifiable sensitive fields in a service field set consisting of the service fields as fields to be processed;

judging whether the field to be processed is a newly added sensitive field or not;

if yes, marking the field to be processed which is determined as the newly added sensitive field as a marked field, and adding the marked field into an identifiable sensitive field set configured by the service gateway.

Optionally, after the step of marking the field to be processed, which is determined as the newly added sensitive field, as the marked field is executed, the computer-executable instruction further includes:

determining a tag field and a corresponding field value in service data;

and carrying out data desensitization treatment on the field value corresponding to the marked field to obtain a desensitized field value serving as a target field value, and uploading the desensitized field value to a server.

Optionally, when the computer executable instruction is executed, whether the field to be processed is the newly added sensitive field is determined by the following method:

Optionally, the computer executable instructions, when executed, further implement the following process:

calculating a second information entropy of the field to be processed based on the sample field value of the field to be processed;

judging whether the second information entropy is smaller than a second information entropy threshold value or not;

and if so, determining the field to be processed with the second information entropy smaller than the second information entropy threshold value as the public service field.

Optionally, when executed, the computer executable instruction is applied to a service gateway, where the service gateway is deployed on a client, and performs sampling processing on service data to obtain a service field and a corresponding sample field value, where the method includes:

Optionally, the computer-executable instructions, when executed, cause the set of identifiable sensitive fields to comprise identifiable sensitive fields; determining the service fields except the identifiable sensitive fields in the service field set composed of the service fields as the fields to be processed, including:

Optionally, when the computer-executable instruction is executed, before the step of determining whether the first information entropy is greater than the first information entropy threshold is executed, the following process is further implemented:

determining a first service type corresponding to the service data;

and determining the information entropy threshold corresponding to the first service type as a first information entropy threshold.

Optionally, when executed, the computer executable instruction performs data desensitization processing on a field value corresponding to the tag field, obtains the desensitized field value as a target field value, and uploads the target field value to the server, where the data desensitization processing includes:

determining a second service type corresponding to the service data;

Further, based on the same technical concept, one or more embodiments of the present specification further provide a storage medium for storing computer-executable instructions, where in a specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, and the like, and when the computer-executable instructions stored in the storage medium are executed by a processor, the following process can be implemented:

Optionally, after the step of marking the field to be processed, which is determined as the newly added sensitive field, as the mark field is executed, the computer-executable instructions stored in the storage medium further include:

determining a tag field and a corresponding field value in service data;

Optionally, when the computer-executable instruction stored in the storage medium is executed by the processor, whether the field to be processed is the newly added sensitive field is determined by:

Optionally, the storage medium stores computer-executable instructions, which when executed by the processor, further implement the following process:

Optionally, when executed by the processor, the computer-executable instructions stored in the storage medium are applied to a service gateway, where the service gateway is deployed on a client, and performs sampling processing on service data to obtain a service field and a corresponding sample field value, where the method includes:

Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, cause the set of identifiable sensitive fields to contain identifiable sensitive fields; determining the service fields except the identifiable sensitive fields in the service field set composed of the service fields as the fields to be processed, including:

Optionally, the computer-executable instructions stored in the storage medium, when executed by the processor, before the step of determining whether the first information entropy is greater than the first information entropy threshold is executed, further include:

determining a first service type corresponding to the service data;

Optionally, when executed by the processor, the computer-executable instructions stored in the storage medium perform data desensitization processing on a field value corresponding to the tag field, obtain a desensitized field value as a target field value, and upload the target field value to the server, where the method includes:

determining a second service type corresponding to the service data;

It should be noted that the embodiment related to the storage medium in this specification and the embodiment related to the service data processing method in this specification are based on the same inventive concept, and therefore, specific implementation of this embodiment may refer to implementation of the foregoing corresponding method, and repeated details are not described here.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 30 s of the 20 th century, improvements in a technology could clearly be distinguished between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in multiple software and/or hardware when implementing the embodiments of the present description.

One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.

Claims

1. A service data processing method comprises the following steps:

judging whether the field to be processed is a newly added sensitive field;

2. The method of claim 1, after the step of marking the field to be processed determined as the newly added sensitive field as the mark field is executed, further comprising:

determining the tag field and a corresponding field value in the service data;

3. The method of claim 1, wherein whether the field to be processed is a newly added sensitive field is determined by:

calculating a first information entropy of the field to be processed based on a sample field value of the field to be processed;

4. The method of claim 1, further comprising:

and if so, determining the field to be processed with the second information entropy smaller than the second information entropy threshold value as a public service field.

5. The method of claim 1, applied to the service gateway, the service gateway being deployed on a client, and the sampling service data to obtain a service field and a corresponding sample field value, including:

acquiring service data generated by the client, wherein the service data comprises the service field and a corresponding field value to be sampled;

6. The method of claim 5, the set of identifiable sensitive fields comprising the identifiable sensitive field; determining the service fields except the identifiable sensitive fields in the service field set composed of the service fields as fields to be processed, including:

judging whether the service fields in the service field set are the identifiable sensitive fields in the identifiable sensitive field set;

and if not, determining the service field which is determined to be not the recognizable sensitive field as the field to be processed.

7. The method of claim 3, wherein before the step of determining whether the first information entropy is greater than the first information entropy threshold is executed, the method further comprises:

determining a first service type corresponding to the service data;

and determining the information entropy threshold corresponding to the first service type as the first information entropy threshold.

8. The method according to claim 2, wherein performing data desensitization processing on the field value corresponding to the tag field to obtain a desensitized field value as a target field value for uploading to a server, includes:

determining a second service type corresponding to the service data;

and according to the target desensitization format, carrying out data desensitization treatment on the field value corresponding to the marked field to obtain a desensitized field value serving as the target field value, and uploading the desensitized field value to the server.

9. A service data processing apparatus, comprising:

the data sampling module is configured to sample the service data to obtain a service field and a corresponding sample field value;

a field to be processed determining module configured to determine a service field other than an identifiable sensitive field in a service field set composed of the service fields as a field to be processed;

a newly added sensitive field determining module configured to determine whether the field to be processed is a newly added sensitive field;

and the field marking module is configured to mark the field to be processed which is determined as the newly added sensitive field as a marked field, and add the marked field to an identifiable sensitive field set configured by the service gateway.

10. A service data processing apparatus comprising:

a memory and a processor;

the memory is to store computer-executable instructions, and the processor is to execute the computer-executable instructions to:

judging whether the field to be processed is a newly added sensitive field;

11. A storage medium storing computer-executable instructions that when executed implement the following:

judging whether the field to be processed is a newly added sensitive field;