CN116090019B - Privacy computing method and system based on distributed collaboration - Google Patents
Privacy computing method and system based on distributed collaboration Download PDFInfo
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- CN116090019B CN116090019B CN202310387442.5A CN202310387442A CN116090019B CN 116090019 B CN116090019 B CN 116090019B CN 202310387442 A CN202310387442 A CN 202310387442A CN 116090019 B CN116090019 B CN 116090019B
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Abstract
The invention belongs to the field of privacy computation, relates to a data analysis technology, and aims to solve the problem that the existing privacy computation system cannot analyze the hardware running state of a computation space and the influence of data provided by a data provider on processing efficiency, in particular to a privacy computation method and a system based on distributed collaboration, wherein the privacy computation method and the system comprise a privacy computation platform which is in communication connection with an identification processing module, a computation analysis module, a period management module, an exception processing module and a storage module; the invention can detect and analyze the calculation processing state of the calculation processing module, and obtain the calculation coefficient by analyzing and calculating the data processing amount and the processing time length of the calculation processing unit, so that the processing efficiency of the calculation processing unit is fed back by the calculation coefficient, and the data supplier and the calculation processing unit are marked when the processing efficiency is abnormal, thereby providing data support for the subsequent calculation processing optimization process.
Description
Technical Field
The invention belongs to the field of privacy calculation, relates to a data analysis technology, and particularly relates to a privacy calculation method and system based on distributed collaboration.
Background
The privacy calculation is a technical set for realizing data analysis and calculation on the premise of protecting the data from external leakage, so as to achieve the purpose of being 'available and invisible' for the data; on the premise of fully protecting data and privacy safety, the conversion and release of data value are realized.
The existing privacy computing system does not have a function of detecting the data processing efficiency of the computing space, so that the hardware running state of the computing space and the influence of data provided by a data provider on the processing efficiency cannot be analyzed, and the data processing efficiency cannot be optimized.
Aiming at the technical problems, the application provides a solution.
Disclosure of Invention
The invention aims to provide a privacy computing method and a privacy computing system based on distributed collaboration, which are used for solving the problem that the existing privacy computing system cannot analyze the hardware running state of a computing space and the influence of data provided by a data provider on processing efficiency;
the technical problems to be solved by the invention are as follows: how to provide a privacy computing method and system based on distributed collaboration, which can analyze the hardware running state of a computing space and the influence of data provided by a data provider on the processing efficiency.
The aim of the invention can be achieved by the following technical scheme:
the privacy computing method and system based on distributed collaboration comprise a privacy computing platform, wherein the privacy computing platform is in communication connection with an identification processing module, a computing analysis module, a period management module, an abnormality processing module and a storage module;
the identification processing module is used for carrying out identification removal processing analysis on the data source: decomposing the calculation data group received by the identification processing module into a plurality of data packets, removing the identification symbols of the data in the data packets, and simultaneously transmitting the data packets with the identification symbols removed to the calculation processing module;
the computing processing module comprises a plurality of computing processing units, wherein the computing processing units are used for carrying out privacy computation on the received data packets and sending privacy computation results to a privacy computing platform;
the calculation analysis module is used for detecting and analyzing the calculation processing state of the calculation processing module: marking a calculation processing unit as a detection object i, i=1, 2, …, n and n are positive integers, acquiring stored data CCi, amount of data CLi and time of treatment data CSi when the detection object i performs privacy calculation, performing numerical calculation to obtain a calculation coefficient JSi of the detection object i, and marking the detection object i as a treatment opposite object or a treatment different object according to the numerical value of the calculation coefficient JSi;
the period management module is used for carrying out periodic management analysis on the detection analysis result of the calculation analysis module;
the exception handling module is used for detecting and analyzing the running environment of the abnormal object.
As a preferred embodiment of the present invention, the stored data CCi is a total value of a packet memory when the detection object performs privacy calculation, the stored data CLi is a total value of a number of data in a packet when the detection object performs privacy calculation, and the stored data CSi is a calculation time when the detection object performs privacy calculation.
As a preferred embodiment of the present invention, the specific process of marking the detection object i as a positive-object or a differential-object includes: the calculation threshold value JSmin is obtained through the storage module, and the calculation coefficient JSi of the detection object i is compared with the calculation threshold value JSmin: if the calculation coefficient JSi is smaller than or equal to the calculation threshold JSmin, judging that the calculation processing state of the detection object i does not meet the requirement, marking the corresponding detection object as a different object, and marking the processing characteristic of a participant in the different object processing data packet as abnormal; if the calculation coefficient JSi is greater than the calculation threshold JSmin, it is determined that the calculation processing state of the detection object i meets the requirement, and the corresponding detection object is marked as a positive object.
As a preferred embodiment of the present invention, the specific process of the periodic management module for performing periodic management analysis on the detection analysis result of the calculation analysis module includes: setting a management period, acquiring different data CY and different data GY in the management period, carrying out numerical calculation on the different data CY and the different data GY to obtain an abnormal coefficient YC of the management period, acquiring an abnormal threshold YCmax through a storage module, and comparing the abnormal coefficient YC with the abnormal threshold YCmax: if the anomaly coefficient YC is smaller than or equal to the anomaly threshold YCmin, judging that the calculation processing state of the calculation processing module in the management period meets the requirement; if the anomaly coefficient YC is larger than the anomaly threshold YCmin, judging that the calculation processing state of the calculation processing module in the management period does not meet the requirement, and carrying out processing supply analysis on the management period.
As a preferred embodiment of the present invention, the process of acquiring the differential data CY and the differential data GY includes: the method comprises the steps of obtaining the times that detection objects are marked as abnormal objects in a management period and marking the abnormal objects as abnormal values, marking L1 detection objects with the largest abnormal value as salient objects, marking the total times that salient objects are marked as abnormal objects as abnormal data CY, obtaining the times that processing features of participators are marked as abnormal in the management period and marking the obtained values as marked values of the participators, marking L2 participators with the largest marked value as abnormal objects, and marking the total times that the processing features of the abnormal objects are marked as abnormal data GY.
As a preferred embodiment of the present invention, the specific process of performing a process supply analysis on a management cycle includes: the method comprises the steps of obtaining the total quantity of data provided by different objects processed by different objects in a management period, marking the total quantity as an association value, marking the ratio of the association value to the total quantity of all data processed by the processed objects in the management period as an association coefficient, obtaining an association threshold value through a storage module, and comparing the association coefficient of all different objects with the association threshold value one by one: if the association coefficient is smaller than the association threshold, judging that the abnormal object exists abnormal, and sending a hardware detection signal to an abnormal processing module by the period management module; if the association coefficient is larger than the association threshold, the abnormal condition of the different object is judged, the period management module sends the different object to the privacy computing platform, and the privacy computing platform sends the different object to the mobile phone terminal of the manager after receiving the different object.
As a preferred embodiment of the invention, the exception handling module detects and analyzes the running environment of the abnormal object: acquiring air temperature data KW, air humidity data KS and air dust data KC of a different object, wherein the air temperature data KW is an air temperature value in the running environment of the different object, the air humidity data KS is an air humidity value in the running environment of the different object, the air dust data KC is an air dust concentration value in the running environment of the different object, and the ring difference coefficient HY of the different object is obtained by carrying out numerical calculation on the air temperature data KW, the air humidity data KS and the air dust data KC; the method comprises the steps of obtaining a ring difference threshold HYmax through a storage module, and comparing the ring difference coefficient HY with the ring difference threshold HYmax: if the ring anomaly coefficient HY is smaller than the ring anomaly threshold HYmax, judging that the cause of anomaly of the abnormal object is irrelevant to the running environment, and sending a mechanical investigation signal to the privacy computing platform by the anomaly processing module; if the ring anomaly coefficient HY is greater than or equal to a ring anomaly threshold HYmax, determining that the cause of anomaly of the abnormal object is related to the running environment, and sending an environment adjusting signal to the privacy computing platform by the anomaly processing module.
A privacy computing method based on distributed collaboration comprises the following steps:
step one: decomposing the calculation data group received by the identification processing module into a plurality of data packets, removing the identification symbols of the data in the data packets, and simultaneously transmitting the data packets with the identification symbols removed to the calculation processing module;
step two: detecting and analyzing the calculation processing state of the calculation processing module: marking a calculation processing unit as a detection object i, i=1, 2, …, n and n are positive integers, obtaining a calculation coefficient JSi of the detection object i, and marking the detection object as a different object or a positive object through the numerical value of the calculation coefficient JSi;
step three: and carrying out periodic management analysis on the detection analysis result of the calculation analysis module: setting a management period, acquiring an abnormal coefficient YC of the management period, judging whether the calculation processing state in the management period meets the requirement or not according to the numerical value of the abnormal coefficient YC, and performing processing supply analysis when the calculation processing state does not meet the requirement;
step four: and detecting and analyzing the running environment of the abnormal object when the abnormal object exists as a result of the processing and supplying analysis.
The invention has the following beneficial effects:
1. the calculation and analysis module can detect and analyze the calculation processing state of the calculation processing module, and the calculation coefficient is obtained by analyzing and calculating the data processing amount and the processing time of the calculation processing unit, so that the processing efficiency of the calculation processing unit is fed back through the calculation coefficient, and the data supplier and the calculation processing unit are marked when the processing efficiency is abnormal, so that data support is provided for the subsequent calculation processing optimization process;
2. the periodic management module is used for carrying out periodic management analysis on the monitoring analysis result of the calculation analysis module, carrying out comprehensive analysis on the abnormal object marking state in the management period and the processing characteristic marking state of the participator, and carrying out processing supply analysis on the abnormal object and the participator, thereby judging the influence factors of the abnormal processing state, and carrying out responsibility division on the abnormal processing efficiency through the judgment result;
3. the abnormal processing module can detect and analyze the running environment of the abnormal object, comprehensively analyze and calculate various environmental parameters of the abnormal object to obtain a ring abnormal coefficient, and examine the real abnormal factors of the abnormal object according to the numerical value of the ring abnormal coefficient, so that the processing efficiency of hardware abnormality is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, the privacy computing system based on distributed collaboration comprises a privacy computing platform, wherein the privacy computing platform is in communication connection with an identification processing module, a computing analysis module, a period management module, an exception processing module and a storage module.
The identification processing module is used for carrying out identification removal processing analysis on the data source: and decomposing the calculation data group received by the identification processing module into a plurality of data packets, removing the identification symbols of the data in the data packets, and simultaneously transmitting the data packets with the identification symbols removed to the calculation processing module.
The computing processing module comprises a plurality of computing processing units, and the computing processing units are used for carrying out privacy computation on the received data packets and sending privacy computation results to the privacy computing platform.
The calculation analysis module is used for detecting and analyzing the calculation processing state of the calculation processing module: marking a calculation processing unit as a detection object i, i=1, 2, …, n and n are positive integers, and acquiring a position data CCi, a position data CLi and a position time data CSI when the detection object i performs privacy calculation, wherein the position data CCi is a total data packet memory value when the detection object performs privacy calculation, the position data CLi is a total data quantity value in a data packet when the detection object performs privacy calculation, and the position time data CSI is a calculation time length when the detection object performs privacy calculation; obtaining a calculation coefficient JSi of the detection object i according to a formula JSi = (alpha 1 x cci+alpha 2 x cli)/(alpha 3 x csi), wherein the calculation coefficient is a numerical value reflecting the degree of the calculation processing state of the detection object, and the larger the numerical value of the calculation coefficient is, the better the calculation processing state of the detection object is; wherein, alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 > alpha 2 > alpha 3 > 1; the calculation threshold value JSmin is obtained through the storage module, and the calculation coefficient JSi of the detection object i is compared with the calculation threshold value JSmin: if the calculation coefficient JSi is smaller than or equal to the calculation threshold JSmin, judging that the calculation processing state of the detection object i does not meet the requirement, marking the corresponding detection object as a different object, and marking the processing characteristic of a participant in the different object processing data packet as abnormal; if the calculation coefficient JSi is larger than the calculation threshold value JSmin, judging that the calculation processing state of the detection object i meets the requirement, and marking the corresponding detection object as a positive object; and detecting and analyzing the calculation processing state of the calculation processing module, analyzing and calculating the data processing amount and the processing time length of the calculation processing unit to obtain a calculation coefficient, feeding back the processing efficiency of the calculation processing unit through the calculation coefficient, marking the data supplier and the calculation processing unit when the processing efficiency is abnormal, and providing data support for the follow-up calculation processing optimization process.
The period management module is used for carrying out periodic management analysis on the detection analysis result of the calculation analysis module: setting a management period, acquiring the times of the detected objects marked as different objects in the management period and marking the detected objects as different values, marking the L1 detected objects with the largest different values as prominent objects, marking the total times of the prominent objects marked as different objects as different data CY, acquiring the times of the participators marked as abnormal processing characteristics in the management period and marking the acquired values as marked values of the participators, marking the L2 participators with the largest marked values as different objects, wherein the L1 and the L2 are constant in value, and the values of the L1 and the L2 are set by a manager; marking the total number of times that the processing characteristics of the dissimilar objects are marked as abnormal as dissimilar data GY, and obtaining an abnormal coefficient YC of a management period through a formula YC=β1 xCY+β2 xGY, wherein β1 and β2 are both proportionality coefficients, and β1 is larger than β2 and larger than 1; the abnormal coefficient is a numerical value reflecting the abnormal degree of the calculation state in the management period, and the larger the numerical value of the abnormal coefficient is, the worse the calculation state of the calculation processing module in the management period is; the abnormal threshold YCmax is obtained through the storage module, and the abnormal coefficient YC is compared with the abnormal threshold YCmax: if the anomaly coefficient YC is smaller than or equal to the anomaly threshold YCmin, judging that the calculation processing state of the calculation processing module in the management period meets the requirement; if the anomaly coefficient YC is larger than the anomaly threshold YCmin, judging that the calculation processing state of the calculation processing module in the management period does not meet the requirement, and performing processing supply analysis on the management period: the method comprises the steps of obtaining the total quantity of data provided by different objects processed by different objects in a management period, marking the total quantity as an association value, marking the ratio of the association value to the total quantity of all data processed by the processed objects in the management period as an association coefficient, obtaining an association threshold value through a storage module, and comparing the association coefficient of all different objects with the association threshold value one by one: if the association coefficient is smaller than the association threshold, judging that the abnormal object exists abnormal, and sending a hardware detection signal to an abnormal processing module by the period management module; if the association coefficient is larger than the association threshold, determining that the different object is abnormal, and sending the different object to the privacy computing platform by the period management module, wherein the privacy computing platform sends the different object to a mobile phone terminal of a manager after receiving the different object; and (3) carrying out periodic management analysis on the monitoring analysis result of the calculation analysis module, carrying out comprehensive analysis on the abnormal treatment object marking state and the treatment characteristic marking state of the participator in the management period, and carrying out treatment supply analysis on the abnormal treatment object and the participator, thereby judging the influence factors of the treatment state abnormality, and carrying out responsibility division on the treatment efficiency abnormality through the judgment result.
The exception handling module is used for detecting and analyzing the running environment of the abnormal object after receiving the hardware detection signal: acquiring air temperature data KW, air humidity data KS and air dust data KC of a different object, wherein the air temperature data KW is an air temperature value in the running environment of the different object, the air humidity data KS is an air humidity value in the running environment of the different object, the air dust data KC is an air dust concentration value in the running environment of the different object, and a ring difference coefficient HY of the different object is obtained through a formula HY=γ1xKW+γ2xKS+γ3xKC, wherein γ1, γ2 and γ3 are proportionality coefficients, γ1 > γ2 > γ3 > 1, the ring difference coefficient is a numerical value reflecting the abnormal degree of the running environment of the different object, and the larger the numerical value of the ring difference coefficient is, the more abnormal the running environment of the different object is represented; the method comprises the steps of obtaining a ring difference threshold HYmax through a storage module, and comparing the ring difference coefficient HY with the ring difference threshold HYmax: if the ring anomaly coefficient HY is smaller than the ring anomaly threshold HYmax, judging that the cause of anomaly of the abnormal object is irrelevant to the running environment, and sending a mechanical investigation signal to the privacy computing platform by the anomaly processing module; if the ring anomaly coefficient HY is greater than or equal to a ring anomaly threshold HYmax, judging that the cause of anomaly of the abnormal object is related to the running environment, and sending an environment adjusting signal to the privacy computing platform by the anomaly processing module; detecting and analyzing the running environment of the different object, comprehensively analyzing and calculating various environment parameters of the different object to obtain a ring difference coefficient, and checking the real abnormal factors of the different object according to the numerical value of the ring difference coefficient, so that the processing efficiency of hardware abnormality is improved.
Example two
As shown in fig. 2, a privacy calculating method based on distributed collaboration includes the following steps:
step one: decomposing the calculation data group received by the identification processing module into a plurality of data packets, removing the identification symbols of the data in the data packets, and simultaneously transmitting the data packets with the identification symbols removed to the calculation processing module;
step two: detecting and analyzing the calculation processing state of the calculation processing module: marking a calculation processing unit as a detection object i, i=1, 2, …, n and n are positive integers, obtaining a calculation coefficient JSi of the detection object i, and marking the detection object as a different object or a positive object through the numerical value of the calculation coefficient JSi;
step three: and carrying out periodic management analysis on the detection analysis result of the calculation analysis module: setting a management period, acquiring an abnormal coefficient YC of the management period, judging whether the calculation processing state in the management period meets the requirement or not according to the numerical value of the abnormal coefficient YC, and performing processing supply analysis when the calculation processing state does not meet the requirement;
step four: and detecting and analyzing the running environment of the abnormal object when the abnormal object exists as a result of the processing and supplying analysis.
The privacy computing method and the privacy computing system based on distributed collaboration are characterized in that during operation, a computing data set received by an identification processing module is decomposed into a plurality of data packets, identification symbols of data in the data packets are removed, and meanwhile the data packets with the identification symbols removed are sent to the computing processing module; detecting and analyzing the calculation processing state of the calculation processing module: marking a calculation processing unit as a detection object i, i=1, 2, …, n and n are positive integers, obtaining a calculation coefficient JSi of the detection object i, and marking the detection object as a different object or a positive object through the numerical value of the calculation coefficient JSi; and carrying out periodic management analysis on the detection analysis result of the calculation analysis module: setting a management period, acquiring an abnormal coefficient YC of the management period, judging whether the calculation processing state in the management period meets the requirement or not according to the numerical value of the abnormal coefficient YC, and performing processing supply analysis when the calculation processing state does not meet the requirement; and detecting and analyzing the running environment of the abnormal object when the abnormal object exists as a result of the processing and supplying analysis.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula JSi = (α1×cci+α2×cli)/(α3×csi); collecting a plurality of groups of sample data by a person skilled in the art and setting corresponding calculation coefficients for each group of sample data; substituting the set calculation coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are 3.74, 2.97 and 2.65 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding calculation coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the calculation coefficient is in direct proportion to the value of the stored data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (2)
1. The privacy computing system based on distributed collaboration is characterized by comprising a privacy computing platform, wherein the privacy computing platform is in communication connection with an identification processing module, a computing analysis module, a period management module, an exception processing module and a storage module;
the identification processing module is used for carrying out identification removal processing analysis on the data source: decomposing the calculation data group received by the identification processing module into a plurality of data packets, removing the identification symbols of the data in the data packets, and simultaneously transmitting the data packets with the identification symbols removed to the calculation processing module;
the computing processing module comprises a plurality of computing processing units, wherein the computing processing units are used for carrying out privacy computation on the received data packets and sending privacy computation results to a privacy computing platform;
the calculation analysis module is used for detecting and analyzing the calculation processing state of the calculation processing module: marking a calculation processing unit as a detection object i, i=1, 2, …, n and n are positive integers, acquiring stored data CCi, amount of data CLi and time of treatment data CSi when the detection object i performs privacy calculation, performing numerical calculation to obtain a calculation coefficient JSi of the detection object i, and marking the detection object i as a treatment opposite object or a treatment different object according to the numerical value of the calculation coefficient JSi;
the period management module is used for carrying out periodic management analysis on the detection analysis result of the calculation analysis module;
the exception handling module is used for detecting and analyzing the running environment of the abnormal object;
the method comprises the steps that stored data CCi is a total value of a data packet memory when a detection object performs privacy calculation, the amount of data CLi is a total value of data quantity in a data packet when the detection object performs privacy calculation, and the amount of data CSI is calculation time when the detection object performs privacy calculation;
the specific process of marking the detected object i as a positive object or a different object comprises the following steps: the calculation threshold value JSmin is obtained through the storage module, and the calculation coefficient JSi of the detection object i is compared with the calculation threshold value JSmin: if the calculation coefficient JSi is smaller than or equal to the calculation threshold JSmin, judging that the calculation processing state of the detection object i does not meet the requirement, marking the corresponding detection object as a different object, and marking the processing characteristic of a participant in the different object processing data packet as abnormal; if the calculation coefficient JSi is larger than the calculation threshold value JSmin, judging that the calculation processing state of the detection object i meets the requirement, and marking the corresponding detection object as a positive object;
the specific process of the periodic management module for carrying out periodic management analysis on the detection analysis result of the calculation analysis module comprises the following steps: setting a management period, acquiring different data CY and different data GY in the management period, carrying out numerical calculation on the different data CY and the different data GY to obtain an abnormal coefficient YC of the management period, acquiring an abnormal threshold YCmax through a storage module, and comparing the abnormal coefficient YC with the abnormal threshold YCmax: if the anomaly coefficient YC is smaller than or equal to the anomaly threshold YCmin, judging that the calculation processing state of the calculation processing module in the management period meets the requirement; if the anomaly coefficient YC is larger than the anomaly threshold YCmin, judging that the calculation processing state of the calculation processing module in the management period does not meet the requirement, and performing processing supply analysis on the management period;
the process for obtaining the different data CY and the different data GY comprises the following steps: the method comprises the steps of obtaining the times that detection objects are marked as abnormal objects in a management period and marking the detection objects as abnormal values, marking L1 detection objects with the largest abnormal value as salient objects, marking the total times that the salient objects are marked as abnormal objects as abnormal data CY, obtaining the times that processing features of participators are marked as abnormal in the management period and marking the obtained values as marked values of the participators, marking L2 participators with the largest marked value as abnormal objects, and marking the total times that the processing features of the abnormal objects are marked as abnormal data GY;
the specific process of processing and supplying analysis to the management period comprises the following steps: the method comprises the steps of obtaining the total quantity of data provided by different objects processed by different objects in a management period, marking the total quantity as an association value, marking the ratio of the association value to the total quantity of all data processed by the processed objects in the management period as an association coefficient, obtaining an association threshold value through a storage module, and comparing the association coefficient of all different objects with the association threshold value one by one: if the association coefficient is smaller than the association threshold, judging that the abnormal object exists abnormal, and sending a hardware detection signal to an abnormal processing module by the period management module; if the association coefficient is larger than the association threshold, the abnormal condition of the different object is judged, the period management module sends the different object to the privacy computing platform, and the privacy computing platform sends the different object to the mobile phone terminal of the manager after receiving the different object.
2. The privacy computing system based on distributed collaboration according to claim 1, wherein the exception handling module performs detection analysis on the running environment of the alien object: acquiring air temperature data KW, air humidity data KS and air dust data KC of a different object, wherein the air temperature data KW is an air temperature value in the running environment of the different object, the air humidity data KS is an air humidity value in the running environment of the different object, the air dust data KC is an air dust concentration value in the running environment of the different object, and the ring difference coefficient HY of the different object is obtained by carrying out numerical calculation on the air temperature data KW, the air humidity data KS and the air dust data KC; the method comprises the steps of obtaining a ring difference threshold HYmax through a storage module, and comparing the ring difference coefficient HY with the ring difference threshold HYmax: if the ring anomaly coefficient HY is smaller than the ring anomaly threshold HYmax, judging that the cause of anomaly of the abnormal object is irrelevant to the running environment, and sending a mechanical investigation signal to the privacy computing platform by the anomaly processing module; if the ring anomaly coefficient HY is greater than or equal to a ring anomaly threshold HYmax, determining that the cause of anomaly of the abnormal object is related to the running environment, and sending an environment adjusting signal to the privacy computing platform by the anomaly processing module.
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| CN110087099B (en) * | 2019-03-11 | 2020-08-07 | 北京大学 | Monitoring method and system for protecting privacy |
| CN110210246B (en) * | 2019-05-31 | 2022-01-07 | 创新先进技术有限公司 | Personal data service method and system based on safety calculation |
| CN114036068A (en) * | 2021-12-13 | 2022-02-11 | 中国平安财产保险股份有限公司 | Update detection method, device, equipment and storage medium based on privacy security |
| CN114417428B (en) * | 2022-03-30 | 2022-08-26 | 天聚地合(苏州)科技股份有限公司 | Privacy calculation method and system based on distributed cooperation |
| CN115296903A (en) * | 2022-08-04 | 2022-11-04 | 国家信息中心 | Data security supervision method based on privacy calculation |
| CN115396168A (en) * | 2022-08-18 | 2022-11-25 | 上海阵方科技有限公司 | Privacy calculation user supervision system based on block chain technology |
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