CN111159754A - Data desensitization method and device for reverse analysis - Google Patents
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Abstract
The invention discloses a data desensitization method and a data desensitization device for reverse analysis, wherein the method comprises the following steps: receiving an SQL statement of query data sent by a user; judging whether the SQL statement contains a target list expression of fields related to sensitive data, and if not, terminating the processing of the SQL statement; judging whether the SQL statement has nested query or not; recursion layer by layer from inside to outside until the target list expression containing the field related to the sensitive data is positioned in the nested query of the outermost layer in the SQL statement; converting the target list expression of the SQL statement according to a preset desensitization conversion rule, so that sensitive data accessed by the converted SQL statement is desensitized data; outputting data to be queried by an SQL statement; and a corresponding apparatus is disclosed. The method for reversely and automatically analyzing the field evolution footprint based on the SQL semantics intelligently analyzes whether the final data result is desensitized.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data desensitization method and device for reverse analysis.
Background
The premise that the data center station provides data-oriented application support is to guarantee data security. Desensitization of data related to sensitive information is a common and efficient method for better protection of sensitive information, such as personal information.
Data desensitization (Data Masking), also known as Data bleaching, Data de-privacy, or Data morphing. The query SQL statement for querying data by a user is generally an SQL SELECT statement, and is denoted as SELECT a FROM B, where a is a query expression and B is a data table, that is, data satisfying the query expression a is selected FROM the data table B. When a user queries data, if fields in the target list expression relate to sensitive data, such as personal information related to names, contact numbers, identification numbers, wage amounts and the like of people, desensitization processing is often required on the data.
If data security is guaranteed, desensitization treatment can be directly carried out on a field of a source database, but the desensitization treatment easily causes some problems, for example, desensitization treatment for reserving surnames is directly carried out on client names, unique counting of the client names is realized, and obvious differences occur in statistical results. Therefore, it cannot be handled at the source.
Sometimes, some data analysis applications need to be supported by complex query SQL statements, which often involve multiple tables, nesting, association, union, calculation, require intelligent identification of field evolution footprints, and determine whether desensitization processing is needed or not and how conversion processing is needed on the presentation of final results for sensitive fields in some known tables. In this regard, conventionally, individual SQL statements are manually identified and manually processed and converted into new SQL statements. This approach is inefficient, without intelligent analysis, and does not allow batch processing and intelligent conversion.
To solve this problem, it is currently common practice to parse an SQL statement into an abstract syntax tree, traverse the abstract syntax tree using a VISITOR mode, starting from a root node to a last leaf node, and continuously collect information into a context during the traversal process, and after the whole traversal process is completed, the syntax meaning expressed for the tree is already stored into the context. In brief, the SQL statement analysis is a process from top to bottom and from outside to inside, and the process of evolution from inside to outside cannot be traced under the condition that the fields of the table are known, so that the final evolution result cannot be obtained.
Disclosure of Invention
The invention provides a data desensitization method and a data desensitization device for reverse analysis, which aim to solve the technical problems.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
according to a first aspect of the embodiments of the present invention, there is provided a data desensitization method of reverse analysis, including the steps of:
104, recursing layer by layer from inside to outside until a target list expression containing fields related to sensitive data is positioned in the nested query of the outermost layer in the SQL statement;
105, converting the target list expression of the SQL statement according to a preset desensitization conversion rule, so that sensitive data accessed by the converted SQL statement is desensitized data;
and 106, outputting the data to be queried by the SQL statement.
Preferably, the step 101 includes:
step 1011, when receiving the SQL statement of the query data sent by the user, verifying the query authority of the user and selecting the desensitization conversion rule corresponding to the query authority of the user.
Preferably, the step 101 includes:
step 1012, determining whether the SQL statement contains a data table to be queried, and if not, terminating the processing of the SQL statement.
Preferably, the step 105 comprises:
and 1051, judging whether the target list expression of the SQL statement needs desensitization treatment, and if not, directly jumping to the step 106.
Preferably, the desensitization transformation rule comprises:
and replacing the statement used for accessing the sensitive data in the target list expression of the SQL statement with a preset conversion function, wherein the preset conversion function is used for converting the characters at the specified position in the sensitive data into preset characters.
According to a second aspect of the embodiments of the present invention, there is provided a reverse-analytic data desensitization apparatus, including:
the instruction receiving module is used for receiving SQL sentences of query data sent by a user;
the first judgment module is used for judging whether the SQL statement contains a target list expression of fields related to sensitive data or not, and if not, the processing of the SQL statement is terminated;
the second judgment module is used for judging whether the SQL statement has nested query or not, and if not, the SQL statement is output to the desensitization conversion module;
the recursion module is used for recursion layer by layer from inside to outside until a target list expression containing fields related to sensitive data is positioned in the nested query of the outermost layer in the SQL statement;
the desensitization conversion module is used for converting the target list expression of the SQL statement according to a preset desensitization conversion rule so that sensitive data accessed by the converted SQL statement is desensitized data;
and the data output module is used for outputting the data to be queried by the SQL statement.
Preferably, the instruction receiving module includes:
and the permission verification sub-module is used for verifying the query permission of the user and selecting a desensitization conversion rule corresponding to the query permission of the user when receiving the SQL statement of the query data sent by the user.
Preferably, the instruction receiving module includes:
and the third judgment module is used for judging whether the SQL statement contains a data table which needs to be inquired, and if not, the processing of the SQL statement is terminated.
Preferably, the desensitization conversion module comprises:
and the fourth judgment module is used for judging whether the target list expression of the SQL statement needs desensitization treatment or not, and if not, directly jumping to the data output module.
Preferably, the desensitization transformation rule comprises:
and replacing the statement used for accessing the sensitive data in the SQL statement with a preset conversion function, wherein the preset conversion function is used for converting the characters at the specified position in the sensitive data into preset characters.
Compared with the prior art, the method for reversely and automatically analyzing the field evolution footprint based on the SQL semantics judges whether the final data result needs desensitization treatment or not, and if so, carries out desensitization treatment; the method can be used for data desensitization, can be used for intelligent batch processing, does not need a user to be familiar with various databases, has high technical level, and does not need excessive business training of the user, so that the method does not need to manually intervene and modify one SQL statement, greatly saves the labor cost, and enhances the data safety.
Drawings
FIG. 1 is a flow chart of a method of inverse analytical data desensitization according to the present invention;
FIG. 2 is a block diagram of a data desensitization apparatus according to the present invention.
In the figure, 201-an instruction receiving module, 202-a first judgment module, 203-a second judgment module, 204-a recursion module, 205-a desensitization conversion module and 206-a data output module.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments shown in the drawings. These embodiments are not intended to limit the present invention, and structural, methodological, or functional changes made by those skilled in the art according to these embodiments are included in the scope of the present invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, a method for desensitizing data by reverse analysis includes the following steps:
In order to prevent the SQL statement from being invalid, after receiving the SQL statement of the query data sent by the user, the method further comprises the following invalid query substeps:
and step 1011, judging whether the SQL statement contains a data table to be inquired, and if not, terminating the processing of the SQL statement.
Here, the SQL statement can be accessed by self-definition, and the query select statement included in the SQL instruction included in the SQL statement and the data table to be queried in the query select statement are found by traversing from top to bottom and from outside to inside.
In addition, when the query authority of the user is differentiated, the step 101 may further include an authority verification sub-step of: and when receiving an SQL statement of query data sent by a user, verifying the query authority of the user and selecting a desensitization conversion rule corresponding to the query authority of the user. Different corresponding desensitization conversion rules can be correspondingly set according to different query authorities of users, and are specifically set according to actual needs.
And 102, judging whether the SQL statement contains a target list expression of fields related to sensitive data, and if not, terminating the processing of the SQL statement.
The target list expression of the SQL statement is first found to determine the data fields to be queried, and the sensitive data involved therein. Here, the sensitive data includes at least: name, identification number, telephone number, personal income and address.
If the query field related to sensitive data does not exist, the SQL statement does not need desensitization processing, and the trace-back field has no footprint.
And 103, judging whether the SQL statement has nested query or not, and jumping to the step 105 if not.
Sometimes, complex query SQL statements often involve multiple tables, nesting, association, union, computation, and need to find out where the target list expression of fields relating to sensitive data is specifically located, thereby formulating a corresponding desensitization policy.
And 104, recursing layer by layer from inside to outside until the target list expression containing the fields related to the sensitive data is positioned in the nested query of the outermost layer in the SQL statement.
When an SQL statement has two or more outer nested queries, often the target list expression containing fields relating to sensitive data is located at the inner level of the nest. At this time, repeated recursion is needed, and the recursion is performed layer by layer from inside to outside until no outer layer query is available, and the recursion is stopped. Therefore, through reverse analysis from inside to outside, the field evolves footprints are intelligently identified, whether desensitization processing is needed or not and how the processing is converted if the desensitization processing is needed are judged on the display of final results of sensitive fields in some known data tables.
And 105, converting the target list expression of the SQL statement according to a preset desensitization conversion rule, so that the sensitive data accessed by the converted SQL statement is desensitized data. The converted SQL statement can clearly trace the evolution process of the field containing the sensitive data. When the SQL statement contains the nested query, the converted SQL statement is the SQL statement of which the target list expression in the outermost nested query contains the preset conversion function.
Here, the desensitization conversion rule includes: and replacing the statement used for accessing the sensitive data in the SQL statement with a preset conversion function, wherein the preset conversion function is used for converting the characters at the specified position in the sensitive data into preset characters.
The preset characters may be characters set according to a certain rule, such as: may be replaced with a special symbol such as "+", "#", "%" or the like; may be a random number; the values may be shifted to change the digital data, offset rounded; may be some function with a numerical value correspondence, etc.
The step can also comprise:
and 1051, judging whether the target list expression of the SQL statement needs desensitization treatment, if not, directly jumping to the step 106, and not needing desensitization conversion.
For example, the field to be queried is 'mobile phone number', and the mobile phone number of sensitive information is related. Firstly, an SQLSelectItem object of a field 'mobile phone number' is obtained, an expr attribute object SQLExpr of the SQLSelectItem is researched, and an expression of the change of the field 'mobile phone number' is obtained. If the expression is not calculated and has no function, desensitization processing is required, the expression is intelligently converted into a new expression through a desensitization conversion rule, and a new SQL statement is output; if the expression is the unique count, then the fact that desensitization processing is not needed can be intelligently analyzed, and the 'mobile phone number' is converted into the unique count 'mobile phone number'.
And 106, outputting the data to be queried by the SQL statement.
The converted SQL statement contains a SELECT query statement of a preset conversion function, so that whether the final data result is desensitized or not is judged; the method can be used for data desensitization, can be intelligently processed in batches, does not need manual SQL intervention modification, greatly saves labor cost and enhances data safety.
The data desensitization method of the reverse analysis according to the present invention is further described in detail with reference to the following embodiments.
Assume the following data table: table 1 is the customer table and table 2 is the city table. Two fields of 'mobile phone number' and 'name' in table 1 relate to sensitive data, desensitization processing needs to be performed, and the evolution process of the fields of 'mobile phone number' and 'name' needs to be traced. The desensitization rule for the name field username in data table 1 is set to leave the first bit, other bits are replaced with "+", and expressions may call substr functions such as "concat (username,1,1),". The desensitization rule of the cell number field tel in data table 1 is set to reserve the first three bits, the other bits are replaced with ". times.. times.", and the expression may call substr functions, such as "concat (tel,1,3),'. times.. times.".
| Customer number | Name (I) | Mobile phone number | Income (R) | City number |
| userid | username | tel | sal | cityid |
| 1 | Zhang three | 13612345678 | 2000 | zj |
| 2 | Li four | 13612345679 | 3000 | bj |
| 3 | Wangwu tea | 13812345678 | 3456 | sh |
| 4 | Zhao liu xi | 13712345678 | 4450 | Wh |
| 5 | Zhang Tian (Zhang Tian) | 13612345699 | 5670 | nj |
TABLE 1
| City number | Name of city |
| cityid | cityname |
| zj | Zhejiang river |
| sh | Shanghai province |
| bj | Beijing |
| nj | Nanjing |
| wh | Wuhan dynasty |
TABLE 2
Example 1
The SQL statements that receive query data sent by a user are as follows:
SELECT t.userid,t.sal,t.cityid
FROM cst t
here, the SQL statement does not contain username and tel fields that require desensitization and therefore does not need to be processed.
Example 2
The SQL statements that receive query data sent by a user are as follows:
SELECT t.userid,t.username,t.tel,t.sal,t.cityid
FROM cst t
here, the SQL statement contains two fields, namely username and tel, which need to be desensitized, and the following is shown after query validation and conversion:
SELECT t.userid
,concat(substr(t.username,1,1),'**')AS username
,concat(substr(t.tel,1,3),'***')AS tel
,t.sal,t.cityid
FROM cst t
the output results are shown in table 3:
| userid | username | tel | sal | cityid |
| 1 | zhang Xin | 136*** | 2000 | zj |
| 2 | Plum | 136*** | 3000 | bj |
| 3 | King | 138*** | 3456 | sh |
| 4 | Zhao xi | 137*** | 4450 | wh |
| 5 | Zhang Xin | 136*** | 5670 | nj |
TABLE 3
Example 3:
the SQL statements that receive query data sent by a user are as follows:
SELECT a.uname,a.tel2,a.cityname
From(
SELECT t.userid,t.username as uname,t.tel as tel2,t.sal,t.cityid,t2.cityname
FROM cst t,city t2
WHERE t.cityid＝t2.cityid
)a
here, the SQL statement contains a two-tier query nested inside and outside, where the inner-tier query contains two fields, username and tel, that require desensitization. Therefore, layer-by-layer recursion needs to be performed from inside to outside, and the fields needing desensitization are embodied in the query statement at the outer layer, and after query validation and conversion, the following steps are performed:
SELECT concat(substr(a.uname,1,1),'**')AS uname,concat(substr(a.tel2,1,3),'***')AS tel2,a.cityname
FROM(
SELECT t.userid,t.username AS uname,t.tel AS tel2,t.sal,t.cityid,t2.cityname FROM cst t,city t2
WHERE t.cityid＝t2.cityid
)a
the output results are shown in table 4:
| uname | tel2 | cityname |
| zhang Xin | 136*** | Zhejiang river |
| King | 138*** | Shanghai province |
| Plum | 136*** | Beijing |
| Zhang Xin | 136*** | Nanjing |
| Zhao xi | 137*** | Wuhan dynasty |
TABLE 4
Although the present embodiment shows a basic SQL statement of a nested query, which only includes an inner layer and an outer layer, the present invention is also applicable to SQL statements of nested queries with more than two layers, and SQL statements of nested queries with various complex structures. Finally, the target list expression containing fields relating to sensitive data is ascribed to the outermost nested query in the SQL statement.
Based on the method, the invention also provides a data desensitization device for reverse analysis, which comprises:
the instruction receiving module 201 is configured to receive an SQL statement of query data sent by a user;
the first judging module 202 is configured to judge whether the SQL statement contains a target list expression of a field related to sensitive data, and if not, terminate processing of the SQL statement;
the second judging module 203 is used for judging whether the SQL statement has nested query or not, and if not, outputting the SQL statement to the desensitization conversion module;
the recursion module 204 is used for recursion layer by layer from inside to outside until the target list expression containing the fields related to the sensitive data is positioned in the nested query of the outermost layer in the SQL statement;
the desensitization conversion module 205 is configured to convert the target list expression of the SQL statement according to a preset desensitization conversion rule, so that sensitive data accessed by the converted SQL statement is desensitized data;
and the data output module 206 is used for outputting the data to be queried by the SQL statement.
The instruction receiving module 201 may include an authority verification sub-module, where the authority verification sub-module is configured to verify the query authority of the user and select a desensitization conversion rule corresponding to the query authority of the user when receiving an SQL statement of query data sent by the user.
The instruction receiving module 201 may include a third determining module, where the third determining module is configured to determine whether the SQL statement contains a data table that needs to be queried, and if not, terminate the processing of the SQL statement.
The desensitization conversion module 205 may include a fourth determination module, where the fourth determination module is configured to determine whether the target list expression of the SQL statement needs desensitization processing, and if not, directly skip to the data output module.
Wherein the desensitization transformation rules include:
and replacing the statement used for accessing the sensitive data in the SQL statement with a preset conversion function, wherein the preset conversion function is used for converting the characters at the specified position in the sensitive data into preset characters.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A method of desensitizing data to reverse analysis, comprising the steps of:
step 101, receiving an SQL statement of query data sent by a user;
step 102, judging whether the SQL statement contains a target list expression of fields related to sensitive data, and if not, terminating the processing of the SQL statement;
step 103, judging whether the SQL statement has nested query, and if not, jumping to step 105;
104, recursing layer by layer from inside to outside until a target list expression containing fields related to sensitive data is positioned in the nested query of the outermost layer in the SQL statement;
105, converting the target list expression of the SQL statement according to a preset desensitization conversion rule, so that sensitive data accessed by the converted SQL statement is desensitized data;
and 105, outputting the data to be queried by the SQL statement.
2. A method of desensitizing reverse parsed data according to claim 1, wherein said step 101 comprises:
step 1011, when receiving the SQL statement of the query data sent by the user, verifying the query authority of the user and selecting the desensitization conversion rule corresponding to the query authority of the user.
3. A method of desensitizing reverse parsed data according to claim 1, wherein said step 101 comprises:
step 1012, determining whether the SQL statement contains a data table to be queried, and if not, terminating the processing of the SQL statement.
4. A method of desensitizing reverse parsed data according to claim 1, wherein said step 105 comprises:
and 1051, judging whether the target list expression of the SQL statement needs desensitization treatment, and if not, directly jumping to the step 106.
5. A method of desensitizing reverse parsed data according to any of claims 1 to 4, wherein said desensitization transformation rules comprise:
and replacing the statement used for accessing the sensitive data in the target list expression of the SQL statement with a preset conversion function, wherein the preset conversion function is used for converting the characters at the specified position in the sensitive data into preset characters.
6. A reverse-resolved data desensitization apparatus, comprising:
the instruction receiving module is used for receiving SQL sentences of query data sent by a user;
the first judgment module is used for judging whether the SQL statement contains a target list expression of fields related to sensitive data or not, and if not, the processing of the SQL statement is terminated;
the second judgment module is used for judging whether the SQL statement has nested query or not, and if not, the SQL statement is output to the desensitization conversion module;
the recursion module is used for recursion layer by layer from inside to outside until a target list expression containing fields related to sensitive data is positioned in the nested query of the outermost layer in the SQL statement;
the desensitization conversion module is used for converting the target list expression of the SQL statement according to a preset desensitization conversion rule so that sensitive data accessed by the converted SQL statement is desensitized data;
and the data output module is used for outputting the data to be queried by the SQL statement.
7. A reverse-parsing data desensitization apparatus according to claim 6, wherein said instruction receiving module comprises:
and the permission verification sub-module is used for verifying the query permission of the user and selecting a desensitization conversion rule corresponding to the query permission of the user when receiving the SQL statement of the query data sent by the user.
8. A reverse-parsing data desensitization apparatus according to claim 6, wherein said instruction receiving module comprises:
and the third judgment module is used for judging whether the SQL statement contains a data table which needs to be inquired, and if not, the processing of the SQL statement is terminated.
9. A reverse-analytic data desensitization apparatus according to claim 6, wherein said desensitization conversion module comprises:
and the fourth judgment module is used for judging whether the target list expression of the SQL statement needs desensitization treatment or not, and if not, directly jumping to the data output module.
10. A device for desensitization of reverse parsed data according to any of claims 6 to 9, wherein said desensitization transformation rules comprise:
and replacing the statement used for accessing the sensitive data in the SQL statement with a preset conversion function, wherein the preset conversion function is used for converting the characters at the specified position in the sensitive data into preset characters.
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| CN112052478A (en) * | 2020-09-01 | 2020-12-08 | 上海观安信息技术股份有限公司 | Multi-table subset extraction desensitization method |
| CN112069203A (en) * | 2020-09-22 | 2020-12-11 | 北京百家科技集团有限公司 | Data query method and device |
| CN112765248A (en) * | 2021-01-11 | 2021-05-07 | 上海上讯信息技术股份有限公司 | SQL-based data extraction method and equipment |
| CN112765658A (en) * | 2021-01-15 | 2021-05-07 | 杭州数梦工场科技有限公司 | Data desensitization method and device, electronic equipment and storage medium |
| CN112989412A (en) * | 2021-03-18 | 2021-06-18 | 城云科技（中国）有限公司 | Data desensitization method and device based on SQL statement analysis |
| CN113032836A (en) * | 2021-04-22 | 2021-06-25 | 支付宝(杭州)信息技术有限公司 | Data desensitization method and apparatus |
| CN113792342A (en) * | 2021-09-17 | 2021-12-14 | 平安普惠企业管理有限公司 | Desensitization data restoration method and device, computer equipment and storage medium |
| CN115659391A (en) * | 2022-10-08 | 2023-01-31 | 郑州云智信安安全技术有限公司 | Numerical value desensitization method and device supporting database statistical function |
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| CN112989412A (en) * | 2021-03-18 | 2021-06-18 | 城云科技（中国）有限公司 | Data desensitization method and device based on SQL statement analysis |
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| CN113792342A (en) * | 2021-09-17 | 2021-12-14 | 平安普惠企业管理有限公司 | Desensitization data restoration method and device, computer equipment and storage medium |
| CN113792342B (en) * | 2021-09-17 | 2023-09-08 | 山西数字政府建设运营有限公司 | Desensitization data reduction method, device, computer equipment and storage medium |
| CN115659391A (en) * | 2022-10-08 | 2023-01-31 | 郑州云智信安安全技术有限公司 | Numerical value desensitization method and device supporting database statistical function |
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