Privacy computing data processing method in data element market

Abstract

According to the privacy computing data processing method in the data element market provided by the invention, the collected data is encrypted, so that the security of the data in the transmission and storage process is ensured; by adopting a safe storage mode, the safety of the data in the storage process is ensured; according to characteristics and requirements of data, privacy protection algorithms including homomorphic encryption and differential privacy are selected, and calculation and analysis of encrypted data are achieved. Healthy development of the data element market is promoted through data sharing and utilization; when data leakage occurs, measures including stopping using related data and informing a data source party are taken in time so as to reduce loss; in a data processing process, data security risk assessment is carried out regularly, and potential risk points and data leakage risks in risk assessment are identified; and when the data life cycle is ended, the data are safely deleted, so that the data are ensured not to be leaked and abused any more.

Description

Technical Field

[0001] This invention relates to the field of privacy-preserving computation data processing, and more specifically, to a privacy-preserving computation data processing method in a data element market. Background Technology

[0002] With the development of technologies such as big data, cloud computing, and artificial intelligence, the data factor market has gradually emerged, and data has become an important resource for driving economic and social development. However, the development of the data factor market also faces many challenges, among which privacy protection is particularly prominent. In the data factor market, data often involves sensitive information such as personal privacy and trade secrets.

[0003] However, existing data processing methods have some shortcomings and need improvement. Current privacy-preserving computation technologies typically require that the raw data not be exposed during processing, making data sharing difficult. In scenarios requiring cross-institutional and cross-domain data collaboration, existing privacy-preserving computation technologies struggle to meet data sharing needs, and the lack of data encryption in current data processing poses a risk of data leakage. Therefore, we propose an improvement: a privacy-preserving computation data processing method for the data element market. Summary of the Invention

[0004] The purpose of this invention is to address the problems raised in the existing background technology. To achieve the above-mentioned objective, this invention provides the following technical solution: a privacy-preserving computation data processing method in a data element market, comprising the following steps: Step 1, Data Collection: In the data collection stage, the collected data is encrypted to ensure data security during transmission and storage; homomorphic encryption and differential privacy encryption are used to enable data to complete computation and analysis in an encrypted state. The encryption formula is:

[0005] C1=key×RCON(1)×PAD(block)×SBOX(block)×SHIFT(RCON(1))

[0006] The decryption formula is:

[0007] M1=SHIFT(RCON(1))×SBOX(block)×SHIFT(RCON(1))×PAD(block)×RCON(1)×key;

[0008] Where C1 is the encrypted data, M1 is the decrypted data, key is the key, RCON(1) is the round key configuration, PAD(block) is the block padding, SBOX(block) is the box function, and SHIFT(RCON(1)) is the shift function;

[0009] Step Two, Secure Data Sharing: In the data sharing phase, anonymized data sharing is achieved through a privacy computing platform. The data sharing involves n data points, each with m attributes. A noise term is added to each data point, generated from a specific probability distribution. The variance of the noise term is calculated.

[0010] σ^2=(Δn / (n-1))*(Δm / (m-1))

[0011] Where Δn is the number of data points and Δm is the number of attributes; participants can perform calculations and analyses on encrypted data without directly accessing the original data, thereby protecting the privacy of the data source.

[0012] Step 3, Data Access Control: Set strict data access permissions to ensure that only authorized users and systems can access the data. Data access control: Given a dataset D containing n data points, each data point has m attributes; the risk of data access is assessed using the following formula:

[0013] DataAccessRisk = NumberOfUnauthorizedAccesses / TotalNumberOfAccesses; where DataAccessRisk is the risk of data access, NumberOfUnauthorizedAccesses is the number of unauthorized accesses, and TotalNumberOfAccesses is the total number of all accesses.

[0014] Step 4: Data Usage Supervision: During the data usage phase, strict access control is implemented to ensure that data usage complies with laws, regulations, and ethical standards. Data Access Control: A dataset D is set up, containing n data points, each with m attributes. Each attribute is assigned a permission. The data access control formula is:

[0015] Permission[i]=f(DataPoint[i],Attribute[i])

[0016] Where Permission[i] is the permission of the i-th attribute, DataPoint[i] is the i-th data point, and Attribute[i] is the i-th attribute of the i-th data point;

[0017] Step 5, Secure Data Deletion: At the end of the data lifecycle, securely delete the data to ensure that it is no longer leaked or misused; completely destroy the data through encryption to prevent data leakage and recovery.

[0018] As a preferred technical solution of the present invention, data encryption: using a secure encryption algorithm to encrypt data to ensure data security during transmission and storage; data desensitization: desensitizing sensitive information, including using hash functions and masks, to protect the privacy of the data source.

[0019] As a preferred technical solution of the present invention, data auditing involves periodically auditing data to check whether its use complies with laws, regulations, and ethical standards; data audit monitoring includes a data audit processing flow F, which comprises n steps, each step processing the data; monitoring each step using the following formula:

[0020] Impact[i]=abs(DataAfterStep[i]-DataBeforeStep[i]);

[0021] Where Impact[i] is the impact of the i-th step on the data, DataAfterStep[i] is the data after the i-th step, and DataBeforeStep[i] is the data before the i-th step.

[0022] As a preferred technical solution of the present invention, the data usage record records the usage of data, including the user's identity information, usage time, and usage content; the data is stored securely using a secure storage method, including the use of encrypted cloud storage services, to ensure the security of data during the storage process.

[0023] As a preferred technical solution of this invention, the data privacy protection algorithm selection is as follows: Based on the characteristics and needs of the data, a suitable privacy protection algorithm is selected, including homomorphic encryption and differential privacy; privacy-preserving computation is performed using federated learning and secure multi-party computation for privacy-preserving computation, enabling computation and analysis of encrypted data; data privacy protection is achieved by setting a dataset D containing n data points, each with m attributes; the privacy of each data point is protected using the following formula:

[0024] Privacy[i]=1-(NumberOfMatches[i] / TotalNumberOfDataPoints)

[0025] Where Privacy[i] is the level of privacy protection for the i-th data point, NumberOfMatches[i] is the number of times the i-th data point is matched, and TotalNumberOfDataPoints is the total number of all data points.

[0026] As a preferred technical solution of the present invention, data sharing: under the premise of protecting privacy, data sharing and utilization are realized, promoting the healthy development of the data element market; data leakage emergency handling: in the event of a data leakage, timely measures are taken, including stopping the use of relevant data and notifying the data source, in order to reduce losses.

[0027] As a preferred technical solution of the present invention, data security risk assessment: During the data processing process, data security risk assessment is conducted regularly to identify potential risk points, including the risk of data leakage in the risk assessment;

[0028] Given a dataset D containing n data points, each with m attributes; assess the risk of data leakage using the following formula:

[0029] DataLeakRisk=NumberOfSensitiveDataPoints / TotalNumberOfDataPoints

[0030] Among them, DataLeakRisk represents the risk of data leakage, NumberOfSensitiveDataPoints represents the number of sensitive data points, and TotalNumberOfDataPoints represents the total number of all data points.

[0031] As a preferred technical solution of the present invention, data privacy compliance review: During the data processing process, a privacy compliance review is conducted regularly to ensure that the data processing activities comply with relevant laws, regulations and ethical norms.

[0032] As a preferred technical solution of the present invention, data privacy education and training: providing employees with data privacy protection education and training to improve their privacy protection awareness and skills.

[0033] As a preferred technical solution of the present invention, data privacy incident response: when a data privacy incident occurs, an emergency response is promptly initiated, and effective countermeasures are taken to reduce the impact of the incident on personal privacy and society.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: In the solution of the present invention: by encrypting the collected data during the data collection stage, the security of the data during transmission and storage is ensured; by adopting a secure storage method, including using encrypted cloud storage services, the security of the data during storage is ensured.

[0035] By selecting appropriate privacy-preserving algorithms based on data characteristics and needs, including homomorphic encryption and differential privacy, and by using federated learning and secure multi-party computation for privacy-preserving computation, encrypted data can be computed and analyzed. Under the premise of protecting privacy, data sharing and utilization are achieved, promoting the healthy development of the data element market. Data breach emergency handling: In the event of a data breach, timely measures are taken, including ceasing the use of the relevant data and notifying the data source to minimize losses. Regular data security risk assessments are conducted during data processing to identify potential risk points, including the risk of data breaches. Attached Figure Description

[0036] Figure 1 A flowchart of the method provided by the present invention;

[0037] Figure 2 The method logic block diagram provided by the present invention;

[0038] Figure 3 The method logic block diagram provided by the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0040] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0041] Example 1: Please refer to Figures 1-3 A privacy-preserving computation data processing method in the data element market includes the following steps: Step 1, Data Collection: During the data collection phase, the collected data is encrypted to ensure data security during transmission and storage; homomorphic encryption and differential privacy encryption are used to enable data to be computed and analyzed in an encrypted state. The encryption formula is:

[0042] C1=key×RCON(1)×PAD(block)×SBOX(block)×SHIFT(RCON(1))

[0043] The decryption formula is:

[0044] M1=SHIFT(RCON(1))×SBOX(block)×SHIFT(RCON(1))×PAD(block)×RCON(1)×key

[0045] Where C1 is the encrypted data, M1 is the decrypted data, key is the key, RCON(1) is the round key configuration, PAD(block) is the block padding, SBOX(block) is the box function, and SHIFT(RCON(1)) is the shift function;

[0046] Step Two, Secure Data Sharing: In the data sharing phase, anonymized data sharing is achieved through a privacy computing platform. The data sharing involves n data points, each with m attributes. A noise term is added to each data point, generated from a specific probability distribution. The variance of the noise term is calculated.

[0047] σ^2=(Δn / (n-1))*(Δm / (m-1))

[0048] Where Δn is the number of data points and Δm is the number of attributes; participants can perform calculations and analyses on encrypted data without directly accessing the original data, thereby protecting the privacy of the data source.

[0049] Step 3, Data Access Control: Set strict data access permissions to ensure that only authorized users and systems can access the data. Data access control: Given a dataset D containing n data points, each data point has m attributes; the risk of data access is assessed using the following formula:

[0050] DataAccessRisk = NumberOfUnauthorizedAccesses / TotalNumberOfAccesses; where DataAccessRisk is the risk of data access, NumberOfUnauthorizedAccesses is the number of unauthorized accesses, and TotalNumberOfAccesses is the total number of all accesses.

[0051] Step 4: Data Usage Supervision: During the data usage phase, strict access control is implemented to ensure that data usage complies with laws, regulations, and ethical standards. Data Access Control: A dataset D is set up, containing n data points, each with m attributes. Each attribute is assigned a permission. The data access control formula is:

[0052] Permission[i]=f(DataPoint[i],Attribute[i])

[0053] Where Permission[i] is the permission of the i-th attribute, DataPoint[i] is the i-th data point, and Attribute[i] is the i-th attribute of the i-th data point;

[0054] Step 5: Securely delete the data at the end of its lifecycle to ensure that it is no longer leaked or misused; completely destroy the data through encryption to prevent data leakage and recovery.

[0055] Data encryption: Encrypting data using secure encryption algorithms to ensure data security during transmission and storage; Data anonymization: Anonymizing sensitive information, including using hash functions and masks, to protect the privacy of the data source.

[0056] Data Auditing: Regularly audit data to check whether its use complies with laws, regulations, and ethical standards; Data Audit Monitoring: The data audit process F includes n steps, each processing the data; Monitor each step using the following formula:

[0057] Impact[i]=abs(DataAfterStep[i]-DataBeforeStep[i])

[0058] Where Impact[i] is the impact of the i-th step on the data, DataAfterStep[i] is the data after the i-th step, and DataBeforeStep[i] is the data before the i-th step.

[0059] Data usage records: Record data usage, including user identity information, usage time, and content used; Secure data storage: Employ secure storage methods, including the use of encrypted cloud storage services, to ensure data security during the storage process.

[0060] Data privacy protection algorithm selection: Based on the characteristics and needs of the data, select appropriate privacy protection algorithms, including homomorphic encryption and differential privacy; Privacy-preserving computation: Use federated learning and secure multi-party computation for privacy-preserving computation to achieve computation and analysis of encrypted data; Data privacy protection: By setting up a dataset D containing n data points, each data point has m attributes; Protect the privacy of each data point using the following formula:

[0061] Privacy[i]=1-(NumberOfMatches[i] / TotalNumberOfDataPoints)

[0062] Where Privacy[i] is the level of privacy protection for the i-th data point, NumberOfMatches[i] is the number of times the i-th data point is matched, and TotalNumberOfDataPoints is the total number of all data points.

[0063] Data sharing: Under the premise of protecting privacy, achieve data sharing and utilization to promote the healthy development of the data factor market; Data breach emergency handling: In the event of a data breach, take timely measures, including stopping the use of the relevant data and notifying the data source, to reduce losses.

[0064] Data security risk assessment: During data processing, conduct regular data security risk assessments to identify potential risk points, including the risk of data leakage.

[0065] Given a dataset D containing n data points, each with m attributes; assess the risk of data leakage using the following formula:

[0066] DataLeakRisk=NumberOfSensitiveDataPoints / TotalNumberOfDataPoints

[0067] Among them, DataLeakRisk represents the risk of data leakage, NumberOfSensitiveDataPoints represents the number of sensitive data points, and TotalNumberOfDataPoints represents the total number of all data points.

[0068] Data privacy compliance review: During the data processing process, conduct regular privacy compliance reviews to ensure that data processing activities comply with relevant laws, regulations and ethical standards.

[0069] Data privacy education and training: Provide employees with data privacy protection education and training to improve their privacy awareness and skills.

[0070] Data privacy incident response: In the event of a data privacy incident, an emergency response should be initiated promptly, and effective measures should be taken to reduce the impact of the incident on individual privacy and society.

[0071] In the process of using this invention, during the data collection phase, the collected data is encrypted to ensure data security during transmission and storage; homomorphic encryption and differential privacy encryption are employed to enable calculation and analysis to be performed on the data in an encrypted state. The encryption formula is as follows:

[0072] C1=key×RCON(1)×PAD(block)×SBOX(block)×SHIFT(RCON(1))

[0073] The decryption formula is:

[0074] M1 = SHIFT(RCON(1)) × SBOX(block) × SHIFT(RCON(1)) × PAD(block) × RCON(1) × key; where C1 is the encrypted data, M1 is the decrypted data, key is the key, RCON(1) is the round key configuration, PAD(block) is the block padding, SBOX(block) is the box function, and SHIFT(RCON(1)) is the shift function;

[0075] During the data sharing phase, anonymized data sharing is achieved through a privacy-preserving computing platform. The data sharing involves n data points, each with m attributes. A noise term is added to each data point, generated from a specific probability distribution. The variance of the noise term is then calculated.

[0076] σ^2=(Δn / (n-1))*(Δm / (m-1))

[0077] Where Δn is the number of data points and Δm is the number of attributes; participants can perform calculations and analyses on encrypted data without directly accessing the original data, thereby protecting the privacy of the data source.

[0078] Data access control: Set strict data access permissions to ensure that only authorized users and systems can access the data. Data access control: Given a dataset D containing n data points, each data point has m attributes; the risk of data access is assessed using the following formula:

[0079] DataAccessRisk = NumberOfUnauthorizedAccesses / TotalNumberOfAccesses; where DataAccessRisk is the risk of data access, NumberOfUnauthorizedAccesses is the number of unauthorized accesses, and TotalNumberOfAccesses is the total number of all accesses.

[0080] During the data usage phase, strict access control is implemented to ensure that data use complies with laws, regulations, and ethical standards. Data access control involves a dataset D containing n data points, each with m attributes. Each attribute is assigned a permission, and the data access control formula is as follows:

[0081] Permission[i] = f(DataPoint[i], Attribute[i]); where Permission[i] is the permission of the i-th attribute, DataPoint[i] is the i-th data point, and Attribute[i] is the i-th attribute of the i-th data point; at the end of the data lifecycle, the data is securely deleted to ensure that the data is no longer leaked or misused; the data is completely destroyed by encryption to prevent data leakage and recovery.

[0082] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or substitutions to the present invention, and all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. A method for processing privacy-preserving computational data in a data factor market, characterized in that, Includes the following steps: Step 1: Data Collection: During the data collection phase, the collected data is encrypted to ensure its security during transmission and storage. Homomorphic encryption and differential privacy encryption are used to enable computation and analysis to be performed on the data in an encrypted state. The encryption formula is as follows: C1=key×RCON(1)×PAD(block)×SBOX(block)×SHIFT(RCON(1)) The decryption formula is: M1=SHIFT(RCON(1))×SBOX(block)×SHIFT(RCON(1))×PAD(block)×RCON(1)×key; Where C1 is the encrypted data, M1 is the decrypted data, key is the key, RCON(1) is the key configuration, PAD(block) is the block padding, SBOX(block) is the box function, and SHIFT(RCON(1)) is the shift function; Step Two, Secure Data Sharing: In the data sharing phase, anonymized data sharing is achieved through a privacy computing platform. The data sharing involves n data points, each with m attributes. A noise term is added to each data point, generated from a specific probability distribution. The variance of the noise term is calculated. σ^2=(Δn / (n-1))*(Δm / (m-1)); Where Δn is the number of data points and Δm is the number of attributes; participants can perform calculations and analyses on encrypted data without directly accessing the original data; Step 3, Data Access Control: Set strict data access permissions to ensure that only authorized users and systems can access the data. Data access control is set up with a dataset D containing n data points, each with m attributes. The risk of data access is assessed using the following formula: DataAccessRisk = NumberOfUnauthorizedAccesses / TotalNumberOfAccesses; where DataAccessRisk is the risk of data access, NumberOfUnauthorizedAccesses is the number of unauthorized accesses, and TotalNumberOfAccesses is the total number of all accesses. Step 4: Data Usage Supervision: During the data usage phase, strict access control is implemented to ensure that data usage complies with laws, regulations, and ethical standards. Data Access Control: A dataset D is set up, containing n data points, each with m attributes. Each attribute is assigned a permission. The data access control formula is: Permission[i]=f(DataPoint[i],Attribute[i]) Where Permission[i] is the permission of the i-th attribute, DataPoint[i] is the i-th data point, and Attribute[i] is the i-th attribute of the i-th data point; Step 5: Securely delete the data at the end of its lifecycle to ensure that it is no longer leaked or misused.

2. The privacy-preserving computational data processing method in the data element market according to claim 1, characterized in that, It also includes data encryption: using secure encryption algorithms to encrypt data to ensure data security during transmission and storage; data Desensitization: Sensitive information is desensitized, and the desensitization process includes using hash functions and masks.

3. The privacy-preserving computational data processing method in the data element market according to claim 2, characterized in that, This also includes data auditing: regularly auditing data to check whether its use complies with laws, regulations, and ethical standards; data audit monitoring: a data audit processing flow F, which includes n steps, each processing the data; monitoring each step using the following formula: Impact[i]=abs(DataAfterStep[i]-DataBeforeStep[i]) Where Impact[i] is the impact of the i-th step on the data, DataAfterStep[i] is the data after the i-th step, and DataBeforeStep[i] is the data before the i-th step.

4. The privacy-preserving computational data processing method in the data element market according to claim 3, characterized in that, It also includes data usage records: recording data usage details, including user identity information, usage time, and content used; and secure data storage: employing secure storage methods, including using encrypted cloud storage services, to ensure data security during the storage process.

5. The privacy-preserving computation data processing method in the data element market according to claim 4, characterized in that, It also includes data privacy protection algorithms: computation and analysis of encrypted data; data privacy protection: by setting up a dataset D containing n data points, each data point having m attributes; protecting the privacy of each data point using the following formula: Privacy[i]=1-(NumberOfMatches[i] / TotalNumberOfDataPoints) Where Privacy[i] is the level of privacy protection for the i-th data point, NumberOfMatches[i] is the number of times the i-th data point is matched, and TotalNumberOfDataPoints is the total number of all data points.

6. The privacy-preserving computation data processing method in the data element market according to claim 5, characterized in that, It also includes data sharing: achieving data sharing and utilization while protecting privacy, and promoting the healthy development of the data element market; and emergency handling of data breaches: stopping the use of relevant data and notifying the data source when a data breach occurs.

7. The privacy-preserving computation data processing method in the data element market according to claim 6, characterized in that, This also includes data security risk assessment: During data processing, regular data security risk assessments are conducted to identify potential risk points, including the risk of data leakage. Given a dataset D containing n data points, each with m attributes; assess the risk of data leakage using the following formula: DataLeakRisk = NumberOfSensitiveDataPoints / TotalNumberOfDataPoints, where DataLeakRisk is the risk of data leakage, NumberOfSensitiveDataPoints is the number of sensitive data points, and TotalNumberOfDataPoints is the total number of all data points.

8. The privacy-preserving computational data processing method in the data element market according to claim 7, characterized in that, It also includes data privacy compliance reviews: regular privacy compliance reviews are conducted during the data processing process.

9. The privacy-preserving computational data processing method in the data element market according to claim 8, characterized in that, It also includes data privacy education and training: providing employees with education and training on data privacy protection.

10. The privacy-preserving computational data processing method in the data element market according to claim 9, characterized in that, It also includes data privacy incident response: in the event of a data privacy incident, an emergency response should be initiated promptly.

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