A privacy computing method for trusted data space

Abstract

The present invention provides a privacy computing method for a trusted data space, which relates to the technical field of privacy computing. The method comprises: locally classifying user data, classifying users and performing local encryption processing to determine encrypted privacy data; performing privacy grading processing on the encrypted privacy data to determine the privacy level, superimposing the privacy level, the privacy distribution structure of the encrypted privacy data and the amount of privacy data to perform privacy computing processing on the encrypted privacy data and integrate non-privacy data to transmit to the trusted data space; acquiring transmission data, determining a trusted comprehensive quantitative value of the transmission data according to a preset data evaluation index of the trusted data space, and receiving the transmission data when the trusted comprehensive quantitative value is not lower than a preset trusted value; and effectively ensuring the security and privacy of user data.

Description

Technical Field

[0001] The present invention relates to the field of privacy computing technology, and in particular to a privacy computing method for a trusted data space. Background Art

[0002] With the rapid development of Internet technology, data sharing is becoming more frequent and more and more attention is paid to data security. In the process of data sharing, due to the continuous increase in data, data leakage, tampering and abuse are becoming more and more common, seriously affecting user data privacy and data security. Privacy computing technology can effectively realize the secure sharing of user data while ensuring user privacy through methods such as keys and encrypted data privacy.

[0003] Therefore, the present invention provides a privacy computing method for a trusted data space. Summary of the invention

[0004] The present invention provides a privacy computing method for a trusted data space, which determines encrypted privacy data by performing local classification and user classification and performing local encryption processing on user data; performs privacy grading processing on the encrypted privacy data to determine the privacy level, superimposes the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data to perform privacy computing processing on the encrypted privacy data and integrates the non-privacy data and transmits it to the trusted data space; obtains the transmission data, determines the trusted comprehensive quantitative value of the transmission data according to the preset data evaluation index of the trusted data space, and receives the transmission data when the trusted comprehensive quantitative value is not lower than the preset trusted value; effectively ensures the security and privacy of user data.

[0005] The present invention provides a privacy computing method for a trusted data space, comprising:

[0006] Step 1: Locally classify user data and user classification, determine private data and non-private data, locally encrypt private data, and determine encrypted private data;

[0007] Step 2: Perform privacy grading processing on the encrypted privacy data, determine the privacy level of the encrypted privacy data, superimpose the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data to perform privacy calculation processing on the encrypted privacy data and integrate the non-privacy data to transmit to the trusted data space;

[0008] Step 3: Acquire the transmission data, determine the trusted comprehensive quantitative value of the transmission data according to the preset data evaluation index of the trusted data space, and receive the transmission data when the trusted comprehensive quantitative value is not lower than the preset trusted value.

[0009] According to a privacy computing method for a trusted data space provided by the present invention, user data is locally classified and user classified, private data and non-private data are determined, private data is locally encrypted, and encrypted private data is determined, including:

[0010] Locally classify user data according to the preset data classification strategy, divide the user data at one time, and preliminarily determine the user's private data and non-private data;

[0011] Obtain user classification settings, perform secondary classification on the primary classification results according to the user classification settings, and ultimately determine the user's private data and non-private data;

[0012] When there is no user classification setting, the initially determined privacy data and non-privacy data of the user are determined to be the finally determined privacy data and non-privacy data;

[0013] The privacy data of the finally determined user is locally encrypted to determine the encrypted privacy data.

[0014] According to a privacy computing method for a trusted data space provided by the present invention, local encryption processing is performed on the finally determined private data of the user to determine the encrypted private data, including:

[0015] Perform data desensitization on the privacy data of the finalized user;

[0016] Build a local user data encryption processing model, encrypt and upload model parameters to the trusted data space according to the preset data transmission encryption strategy, and update the model parameters based on the total data encryption processing model of the trusted data space;

[0017] The privacy data of the user who has completed the data desensitization processing is locally encrypted according to the local user data encryption processing model after the model parameter update to determine the encrypted privacy data.

[0018] According to a privacy computing method for a trusted data space provided by the present invention, privacy grading processing is performed on encrypted private data to determine the privacy level of the encrypted private data, including:

[0019] Obtaining the privacy level settings of the trusted data space and the data type sets corresponding to each privacy level;

[0020] Acquire the data type of the encrypted private data, and perform a privacy classification process on the encrypted private data by matching the data type set;

[0021] If the data type of the encrypted private data cannot match the data type set, marking the corresponding encrypted private data as unmatched encrypted private data;

[0022] Obtaining data correlation between unmatched encrypted privacy data and graded encrypted privacy data to perform secondary privacy grading processing;

[0023] The privacy level of the encrypted privacy data is determined according to the result of the first privacy grading processing and the result of the second privacy grading processing.

[0024] According to a privacy computing method for a trusted data space provided by the present invention, the privacy level, the privacy distribution structure of the encrypted private data, and the amount of private data are superimposed to perform privacy computing processing on the encrypted private data and integrate the non-private data to transmit to the trusted data space, including:

[0025] Obtain the transmission channel between the user and the trusted data space, determine the transmission nodes of the transmission channel, the maximum data transmission volume, and the data transmission quality;

[0026] Sorting the transmission channels according to the maximum data transmission amount and the data transmission quality;

[0027] If the amount of data transmitted by the current transmission channel reaches the maximum amount of data transmitted by the current transmission channel, data transmission is performed through the next transmission channel according to the transmission channel sorting result;

[0028] Obtain the completion time of privacy classification processing of encrypted privacy data, and perform transmission data sorting of the encrypted privacy data according to the order of completion time and the transmission channel sorting result;

[0029] Determine the privacy distribution structure of the encrypted privacy data according to the transmission nodes, the transmission data order to be determined, and the privacy level of the encrypted privacy data;

[0030] Determining the amount of private data of the encrypted private data according to the privacy level of the encrypted private data and the corresponding data amount;

[0031] Performing privacy calculation processing on the encrypted privacy data by superimposing the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data, to determine a transmission privacy value of the encrypted privacy data;

[0032] ;in, A transmission privacy value representing encrypted private data; Indicates The amount of encrypted private data; Indicates the total amount of encrypted private data; Indicates Class encrypted private data The amount of data in each transmission channel; Represents the quantized absolute value of the privacy distribution structure of encrypted private data; Indicates the privacy level; Indicates Encrypted private data is at the privacy level The amount of data; The total number of data types representing encrypted private data; Indicates the total number of transmission channels involved in encrypting private data; Indicates the number of privacy levels;

[0033] When the transmission privacy value of the encrypted private data is not lower than the preset transmission value, the trusted data space is verified, and the non-private data is integrated and transmitted to the trusted data space.

[0034] According to a privacy computing method for a trusted data space provided by the present invention, verifying the trusted data space includes:

[0035] Verify the data transmission security of the trusted data space. When data transmission is detected, execute the data transmission security protection strategy and encrypt the transmitted data.

[0036] Perform data consistency check and data security check on the storage data cycle of the trusted data space;

[0037] Verify the access security of the trusted data space, set user access rights, and execute access blocking policies when illegal access is detected.

[0038] According to a privacy computing method for a trusted data space provided by the present invention, a trusted comprehensive quantitative value of transmission data is determined according to a preset data evaluation index of the trusted data space, and when the trusted comprehensive quantitative value is not lower than the preset trusted value, the transmission data is received, including:

[0039] Obtaining preset data evaluation indicators of the trusted data space, performing trusted comprehensive quantification on the transmitted data, and determining the trusted comprehensive quantification value of the transmitted data, wherein the preset data evaluation indicators include: data consistency evaluation indicators, data security evaluation indicators, data integrity evaluation indicators, and data credibility evaluation indicators;

[0040] Determine a trusted comprehensive quantization period of the transmitted data according to the privacy level of the transmitted data, and within the current trusted comprehensive quantization period, when the trusted comprehensive quantization value of the transmitted data is not less than a preset trusted value, receive the transmitted data and reorganize the data according to the privacy distribution structure of the transmitted data;

[0041] When the current trusted comprehensive quantization cycle ends, the remaining transmission data is obtained for secondary trusted comprehensive quantization.

[0042] A privacy computing method for a trusted data space provided by the present invention further includes:

[0043] When there are multiple related users in the trusted data space, determine the multi-user privacy computing target, perform data encryption privacy processing on each user's data, and obtain a multi-user encrypted privacy data set;

[0044] Simulated data nodes are set based on the local location of each user, the simulated data nodes are connected, the encrypted privacy data sets of multiple users are split and combined according to the data types and transmitted to the trusted data space for comprehensive privacy calculation, and the comprehensive privacy calculation value is determined;

[0045] When a user accesses a trusted data space, data decryption access is performed based on the user data of the corresponding user combined with the comprehensive privacy calculation value.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] Local classification and user classification are performed on user data, and local encryption is performed to determine encrypted privacy data; the encrypted privacy data is subjected to privacy grading to determine the privacy level, and the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data are superimposed to perform privacy calculation processing on the encrypted privacy data and integrate the non-privacy data to transmit it to the trusted data space; the trusted comprehensive quantitative value of the transmitted data is determined according to the preset data evaluation index of the trusted data space, and when the trusted comprehensive quantitative value is not lower than the preset trusted value, the transmitted data is received; the security and privacy of user data are effectively ensured.

[0048] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 It is a flowchart of a privacy computing method for a trusted data space provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] Embodiment 1:

[0054] The embodiment of the present invention provides a privacy computing method for a trusted data space, such as Figure 1 As shown, including:

[0055] Step 1: Locally classify user data and user classification, determine private data and non-private data, locally encrypt private data, and determine encrypted private data;

[0056] Step 2: Perform privacy grading processing on the encrypted privacy data, determine the privacy level of the encrypted privacy data, superimpose the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data to perform privacy calculation processing on the encrypted privacy data and integrate the non-privacy data to transmit to the trusted data space;

[0057] Step 3: Acquire the transmission data, determine the trusted comprehensive quantitative value of the transmission data according to the preset data evaluation index of the trusted data space, and receive the transmission data when the trusted comprehensive quantitative value is not lower than the preset trusted value.

[0058] In this embodiment, locally classifying user data refers to classifying the user data according to a preset data classification strategy of the trusted data space.

[0059] In this embodiment, user classification of user data refers to data classification according to user settings. For example, the user sets a1 type data and a2 type data as private data.

[0060] In this embodiment, when the local classification conflicts with the user classification, the user classification result shall prevail.

[0061] In this embodiment, the privacy distribution structure of the encrypted private data refers to the transmission nodes of the encrypted private data and the privacy level distribution structure.

[0062] In this embodiment, the privacy data volume of the encrypted privacy data refers to a value obtained by comprehensively quantifying the data volumes corresponding to each privacy level of the encrypted privacy data.

[0063] In this embodiment, the preset data evaluation indicators of the trusted data space include, for example, a security data evaluation indicator and a consistency data evaluation indicator.

[0064] The working principle and beneficial effects of the above technical solution are: local classification of user data, user classification and local encryption processing to determine encrypted privacy data; privacy grading processing of encrypted privacy data to determine the privacy level, superimposing the privacy level, the privacy distribution structure of the encrypted privacy data and the amount of privacy data to perform privacy calculation processing on the encrypted privacy data and integrate the non-privacy data to transmit to the trusted data space; determining the trusted comprehensive quantitative value of the transmitted data according to the preset data evaluation index of the trusted data space, and receiving the transmitted data when the trusted comprehensive quantitative value is not lower than the preset trusted value; effectively ensuring the security and privacy of user data.

[0065] Embodiment 2:

[0066] The embodiment of the present invention provides a privacy computing method for a trusted data space, which performs local classification and user classification on user data, determines private data and non-private data, performs local encryption processing on the private data, and determines encrypted private data, including:

[0067] Locally classify user data according to the preset data classification strategy, divide the user data at one time, and preliminarily determine the user's private data and non-private data;

[0068] Obtain user classification settings, perform secondary classification on the primary classification results according to the user classification settings, and ultimately determine the user's private data and non-private data;

[0069] When there is no user classification setting, the initially determined privacy data and non-privacy data of the user are determined to be the finally determined privacy data and non-privacy data;

[0070] The privacy data of the finally determined user is locally encrypted to determine the encrypted privacy data.

[0071] In this embodiment, the user classifies and sets, for example, the user sets a1 category data as private data.

[0072] In this embodiment, only the private data is encrypted locally, and the non-private data is not encrypted locally.

[0073] The working principle and beneficial effects of the above technical solution are: local classification of user data and user classification, determination of privacy data and non-privacy data, local encryption of privacy data, and determination of encrypted privacy data, which is conducive to subsequent ensuring of data privacy.

[0074] Embodiment 3:

[0075] The embodiment of the present invention provides a privacy computing method for a trusted data space, which performs local encryption processing on the privacy data of a finally determined user to determine the encrypted privacy data, including:

[0076] Perform data desensitization on the privacy data of the finalized user;

[0077] Build a local user data encryption processing model, encrypt and upload model parameters to the trusted data space according to the preset data transmission encryption strategy, and update the model parameters based on the total data encryption processing model of the trusted data space;

[0078] The privacy data of the user who has completed the data desensitization processing is locally encrypted according to the local user data encryption processing model after the model parameter update to determine the encrypted privacy data.

[0079] In this embodiment, a local user data encryption processing model is constructed for local data encryption.

[0080] In this embodiment, the model parameters are encrypted and uploaded to the trusted data space according to the preset data transmission encryption strategy, and the model parameters are updated based on the total data encryption processing model of the trusted data space to ensure that the local encryption results can be decrypted in the subsequent trusted data space.

[0081] The working principle and beneficial effect of the above technical solution are: by locally encrypting the privacy data of the finally determined user, the encrypted privacy data is determined, thereby ensuring the security of subsequent transmission of the encrypted privacy data.

[0082] Embodiment 4:

[0083] The embodiment of the present invention provides a privacy computing method for a trusted data space, which performs privacy grading processing on encrypted private data and determines the privacy level of the encrypted private data, including:

[0084] Obtaining the privacy level settings of the trusted data space and the data type sets corresponding to each privacy level;

[0085] Acquire the data type of the encrypted private data, and perform a privacy classification process on the encrypted private data by matching the data type set;

[0086] If the data type of the encrypted private data cannot match the data type set, marking the corresponding encrypted private data as unmatched encrypted private data;

[0087] Obtaining data correlation between unmatched encrypted privacy data and graded encrypted privacy data to perform secondary privacy grading processing;

[0088] The privacy level of the encrypted privacy data is determined according to the result of the first privacy grading processing and the result of the second privacy grading processing.

[0089] In this embodiment, the privacy level setting of the trusted data space and the data type sets corresponding to each privacy level are obtained. For example, the trusted data space is set with privacy levels 1, 2, and 3, wherein the data type set corresponding to privacy level 1 is a data type set constructed from a1 data, a2 data, and a3 data.

[0090] In this embodiment, the data correlation between the unmatched encrypted privacy data and the graded encrypted privacy data is obtained to perform secondary privacy grading processing. For example, if there is data correlation between the unmatched encrypted privacy data b1 and the privacy level 1 encrypted privacy data a1, the secondary privacy grading processing will set the unmatched encrypted privacy data b1 to privacy level 1.

[0091] The working principle and beneficial effect of the above technical solution are: by performing privacy grading processing on encrypted privacy data, the privacy level of the encrypted privacy data is determined, which is conducive to subsequent privacy calculations for encrypted privacy data of different privacy levels, further ensuring data privacy and security.

[0092] Embodiment 5:

[0093] The embodiment of the present invention provides a privacy computing method for a trusted data space, which performs privacy computing processing on the encrypted private data by superimposing the privacy level, the privacy distribution structure of the encrypted private data, and the amount of private data, and integrates the non-private data and transmits it to the trusted data space, including:

[0094] Obtain the transmission channel between the user and the trusted data space, determine the transmission nodes of the transmission channel, the maximum data transmission volume, and the data transmission quality;

[0095] Sorting the transmission channels according to the maximum data transmission amount and the data transmission quality;

[0096] If the amount of data transmitted by the current transmission channel reaches the maximum amount of data transmitted by the current transmission channel, data transmission is performed through the next transmission channel according to the transmission channel sorting result;

[0097] Obtain the completion time of privacy classification processing of encrypted privacy data, and perform transmission data sorting of the encrypted privacy data according to the order of completion time and the transmission channel sorting result;

[0098] Determine the privacy distribution structure of the encrypted privacy data according to the transmission nodes, the transmission data order to be determined, and the privacy level of the encrypted privacy data;

[0099] Determining the amount of private data of the encrypted private data according to the privacy level of the encrypted private data and the corresponding data amount;

[0100] Performing privacy calculation processing on the encrypted privacy data by superimposing the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data, to determine a transmission privacy value of the encrypted privacy data;

[0101] ;in, A transmission privacy value representing encrypted private data; Indicates The amount of encrypted private data; Indicates the total amount of encrypted private data; Indicates Class encrypted private data The amount of data in each transmission channel; Represents the quantized absolute value of the privacy distribution structure of encrypted private data; Indicates the privacy level; Indicates Encrypted private data is at the privacy level The amount of data; The total number of data types representing encrypted private data; Indicates the total number of transmission channels involved in encrypting private data; Indicates the number of privacy levels;

[0102] When the transmission privacy value of the encrypted private data is not lower than the preset transmission value, the trusted data space is verified, and the non-private data is integrated and transmitted to the trusted data space.

[0103] In this embodiment, the transmission channels are sorted according to the maximum data transmission volume and the data transmission quality. For example, the transmission channel with high comprehensive data transmission quality and maximum data transmission volume is sorted as the a1 transmission channel, and the transmission channel with low comprehensive data transmission quality and maximum data transmission volume is sorted as the a2 transmission channel.

[0104] In this embodiment, the completion time of the privacy grading processing of the encrypted privacy data is obtained, and the transmission data sorting of the encrypted privacy data is pending according to the completion time sequence combined with the transmission channel sorting result. For example, the completion time t1 of the privacy grading processing of the encrypted privacy data b1 and the completion time t2 of the privacy grading processing of the encrypted privacy data b2 are combined with the a1 transmission channel and the a2 transmission channel. The pending encrypted privacy data b1 is transmitted through the a1 transmission channel, and the encrypted privacy data b2 is transmitted through the a2 transmission channel.

[0105] In this embodiment, the privacy data amount of the encrypted privacy data is determined according to the privacy level of the encrypted privacy data and the corresponding data amount, wherein the privacy data amount refers to the data amount obtained by weighting the corresponding data amount according to the privacy level, and is used to quantify the privacy value of the encrypted privacy data.

[0106] In this embodiment, the encrypted private data may contain data of multiple privacy levels at the same time, and the data amounts of different privacy levels are different. By determining the amount of private data of the encrypted private data, the privacy degree of the encrypted private data is further determined.

[0107] In this embodiment, the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data are superimposed to perform privacy calculation processing on the encrypted privacy data to determine the transmission privacy value of the encrypted privacy data. The higher the transmission privacy value, the higher the security and privacy of the corresponding encrypted privacy data.

[0108] The working principle and beneficial effect of the above technical solution are: by superimposing the privacy level, the privacy distribution structure of the encrypted privacy data and the amount of privacy data, the encrypted privacy data is subjected to privacy calculation processing and the non-privacy data is integrated and transmitted to the trusted data space, thereby effectively ensuring the security and privacy of user data.

[0109] Embodiment 6:

[0110] An embodiment of the present invention provides a privacy computing method for a trusted data space, which verifies the trusted data space, including:

[0111] Verify the data transmission security of the trusted data space. When data transmission is detected, execute the data transmission security protection strategy and encrypt the transmitted data.

[0112] Perform data consistency check and data security check on the storage data cycle of the trusted data space;

[0113] Verify the access security of the trusted data space, set user access rights, and execute access blocking policies when illegal access is detected.

[0114] In this embodiment, the data transmission security of the trusted data space is verified, that is, it is ensured that no data leakage occurs during the process of transferring user data to the trusted data space.

[0115] In this embodiment, data consistency check and data security check are performed on the storage data period of the trusted data space to ensure that no data tampering occurs when data is transferred to the trusted data space.

[0116] In this embodiment, the access security of the trusted data space is verified to avoid data leakage and data abuse.

[0117] The working principle and beneficial effect of the above technical solution are: by verifying the trusted data space, it is used to ensure the security of the user data transmission terminal, thereby ensuring the security of the user data.

[0118] Embodiment 7:

[0119] An embodiment of the present invention provides a privacy computing method for a trusted data space, which determines a trusted comprehensive quantitative value of transmission data according to a preset data evaluation index of the trusted data space, and when the trusted comprehensive quantitative value is not lower than a preset trusted value, receives the transmission data, including:

[0120] Obtaining preset data evaluation indicators of the trusted data space, performing trusted comprehensive quantification on the transmitted data, and determining the trusted comprehensive quantification value of the transmitted data, wherein the preset data evaluation indicators include: data consistency evaluation indicators, data security evaluation indicators, data integrity evaluation indicators, and data credibility evaluation indicators;

[0121] Determine a trusted comprehensive quantization period of the transmitted data according to the privacy level of the transmitted data, and within the current trusted comprehensive quantization period, when the trusted comprehensive quantization value of the transmitted data is not less than a preset trusted value, receive the transmitted data and reorganize the data according to the privacy distribution structure of the transmitted data;

[0122] When the current trusted comprehensive quantization cycle ends, the remaining transmission data is obtained for secondary trusted comprehensive quantization.

[0123] In this embodiment, the trusted integrated quantization period of the transmission data refers to a period of trusted integrated quantization of the transmission data. When the trusted integrated quantization of the transmission data passes, the data transmission corresponding to the trusted integrated quantization period is secure and private.

[0124] In this embodiment, the trusted integrated quantization period of the transmitted data is determined according to the privacy level of the transmitted data. The higher the privacy level of the transmitted data, the shorter the trusted integrated quantization period of the transmitted data, so as to ensure the privacy and security of the transmitted data.

[0125] In this embodiment, when the current trusted comprehensive quantization cycle ends, the remaining transmission data is obtained to perform secondary trusted comprehensive quantization, that is, the security and privacy of the transmission data are re-determined.

[0126] The working principle and beneficial effect of the above technical solution are: the transmission data is quantified in a trusted comprehensive manner according to the preset data evaluation index of the trusted data space, and when the trusted comprehensive quantification value is not lower than the preset trusted value, the transmission data is received, which effectively ensures the security and privacy of user data.

[0127] Embodiment 8:

[0128] An embodiment of the present invention provides a privacy computing method for a trusted data space, further comprising:

[0129] When there are multiple related users in the trusted data space, determine the multi-user privacy computing target, perform data encryption privacy processing on each user's data, and obtain a multi-user encrypted privacy data set;

[0130] Simulated data nodes are set based on the local location of each user, the simulated data nodes are connected, the encrypted privacy data sets of multiple users are split and combined according to the data types and transmitted to the trusted data space for comprehensive privacy calculation, and the comprehensive privacy calculation value is determined;

[0131] When a user accesses a trusted data space, data decryption access is performed based on the user data of the corresponding user combined with the comprehensive privacy calculation value.

[0132] In this embodiment, the goal of multi-user privacy computing is to comprehensively consider each user and maximize the security and privacy of each user's data.

[0133] In this embodiment, the simulation data node is set based on the local location of each user. For example, the simulation data node c1 is correspondingly set for the local location b1 of user a1.

[0134] In this embodiment, the simulated data node refers to a node that simulates a user's local location to perform data transmission.

[0135] In this embodiment, comprehensive privacy calculation refers to performing overall privacy calculation based on the transmission data of all users to determine a comprehensive transmission privacy value.

[0136] In this embodiment, when a user accesses a trusted data space, data decryption access is performed based on the user data of the corresponding user combined with the comprehensive privacy calculation value. For example, when the transmission privacy value of the user data of user a1 is not lower than the preset transmission value and the comprehensive transmission privacy value, user a1 is allowed to perform data decryption access.

[0137] The working principle and beneficial effects of the above technical solution are: by determining the multi-user privacy computing goals, obtaining the multi-user encrypted privacy data set, and performing comprehensive privacy computing, it effectively ensures the data security and data privacy when multiple users transmit data.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A privacy computing method for a trusted data space, characterized in that: include: Step 1: Locally classify user data and user classification, determine private data and non-private data, locally encrypt private data, and determine encrypted private data; Step 2: Perform privacy grading processing on the encrypted privacy data, determine the privacy level of the encrypted privacy data, superimpose the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data to perform privacy calculation processing on the encrypted privacy data and integrate the non-privacy data to transmit to the trusted data space; Perform privacy classification processing on the encrypted privacy data to determine the privacy level of the encrypted privacy data, including: Obtaining the privacy level settings of the trusted data space and the data type sets corresponding to each privacy level; Acquire the data type of the encrypted private data, and perform a privacy classification process on the encrypted private data by matching the data type set; If the data type of the encrypted private data cannot match the data type set, marking the corresponding encrypted private data as unmatched encrypted private data; Obtaining data correlation between unmatched encrypted privacy data and graded encrypted privacy data to perform secondary privacy grading processing; Determine the privacy level of the encrypted privacy data according to the first privacy grading processing result and the second privacy grading processing result; Step 3: Acquire the transmission data, determine the trusted comprehensive quantitative value of the transmission data according to the preset data evaluation index of the trusted data space, and receive the transmission data when the trusted comprehensive quantitative value is not lower than the preset trusted value.

2. The privacy computing method for a trusted data space according to claim 1, characterized in that: Perform local classification and user classification on user data, determine private data and non-private data, perform local encryption on private data, and determine encrypted private data, including: Locally classify user data according to the preset data classification strategy, divide the user data at one time, and preliminarily determine the user's private data and non-private data; Obtain user classification settings, perform secondary classification on the primary classification results according to the user classification settings, and ultimately determine the user's private data and non-private data; When there is no user classification setting, the initially determined privacy data and non-privacy data of the user are determined to be the finally determined privacy data and non-privacy data; The privacy data of the finally determined user is locally encrypted to determine the encrypted privacy data.

3. The privacy computing method for a trusted data space according to claim 2, characterized in that: The finalized user's private data is encrypted locally to determine the encrypted private data, including: Perform data desensitization on the privacy data of the finalized user; Build a local user data encryption processing model, encrypt and upload model parameters to the trusted data space according to the preset data transmission encryption strategy, and update the model parameters based on the total data encryption processing model of the trusted data space; The privacy data of the user who has completed the data desensitization processing is locally encrypted according to the local user data encryption processing model after the model parameter update to determine the encrypted privacy data.

4. The privacy computing method for a trusted data space according to claim 1, characterized in that: The privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data are superimposed to perform privacy calculation processing on the encrypted privacy data and integrate the non-privacy data to transmit to the trusted data space, including: Obtain the transmission channel between the user and the trusted data space, determine the transmission nodes of the transmission channel, the maximum data transmission volume, and the data transmission quality; Sorting the transmission channels according to the maximum data transmission amount and the data transmission quality; If the amount of data transmitted by the current transmission channel reaches the maximum amount of data transmitted by the current transmission channel, data transmission is performed through the next transmission channel according to the transmission channel sorting result; Obtain the completion time of privacy classification processing of encrypted privacy data, and perform transmission data sorting of the encrypted privacy data according to the order of completion time and the transmission channel sorting result; Determine the privacy distribution structure of the encrypted privacy data according to the transmission nodes, the transmission data order to be determined, and the privacy level of the encrypted privacy data; Determining the amount of private data of the encrypted private data according to the privacy level of the encrypted private data and the corresponding data amount; Performing privacy calculation processing on the encrypted privacy data by superimposing the privacy level, the privacy distribution structure of the encrypted privacy data, and the amount of privacy data, to determine a transmission privacy value of the encrypted privacy data; ;in, A transmission privacy value representing encrypted private data; Indicates The amount of encrypted private data; Indicates the total amount of encrypted private data; Indicates Class encrypted private data The amount of data in each transmission channel; Represents the quantized absolute value of the privacy distribution structure of encrypted private data; Indicates the privacy level; Indicates Encrypted private data is at the privacy level The amount of data; The total number of data types representing encrypted private data; Indicates the total number of transmission channels involved in encrypting private data; Indicates the number of privacy levels; When the transmission privacy value of the encrypted private data is not lower than the preset transmission value, the trusted data space is verified, and the non-private data is integrated and transmitted to the trusted data space.

5. The privacy computing method for a trusted data space according to claim 4, characterized in that: Verify the trusted data space, including: Verify the data transmission security of the trusted data space. When data transmission is detected, execute the data transmission security protection strategy and encrypt the transmitted data. Perform data consistency check and data security check on the storage data cycle of the trusted data space; Verify the access security of the trusted data space, set user access rights, and execute access blocking policies when illegal access is detected.

6. The privacy computing method for a trusted data space according to claim 1, characterized in that: Determining a trustworthy comprehensive quantitative value of the transmission data according to a preset data evaluation index of the trustworthy data space, and when the trustworthy comprehensive quantitative value is not lower than the preset trustworthy value, receiving the transmission data, including: Obtaining preset data evaluation indicators of the trusted data space, performing trusted comprehensive quantification on the transmitted data, and determining the trusted comprehensive quantification value of the transmitted data, wherein the preset data evaluation indicators include: data consistency evaluation indicators, data security evaluation indicators, data integrity evaluation indicators, and data credibility evaluation indicators; Determine a trusted comprehensive quantization period of the transmitted data according to the privacy level of the transmitted data, and within the current trusted comprehensive quantization period, when the trusted comprehensive quantization value of the transmitted data is not less than a preset trusted value, receive the transmitted data and reorganize the data according to the privacy distribution structure of the transmitted data; When the current trusted comprehensive quantization cycle ends, the remaining transmission data is obtained for secondary trusted comprehensive quantization.

7. The privacy computing method for a trusted data space according to claim 1, characterized in that: Also includes: When there are multiple related users in the trusted data space, determine the multi-user privacy computing target, perform data encryption privacy processing on each user's data, and obtain a multi-user encrypted privacy data set; Simulated data nodes are set based on the local location of each user, the simulated data nodes are connected, the encrypted privacy data sets of multiple users are split and combined according to the data types and transmitted to the trusted data space for comprehensive privacy calculation, and the comprehensive privacy calculation value is determined; When a user accesses a trusted data space, data decryption access is performed based on the user data of the corresponding user combined with the comprehensive privacy calculation value.

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