CN117216786A - Crowd-sourced platform statistical data on-demand sharing method based on blockchain and differential privacy - Google Patents
Crowd-sourced platform statistical data on-demand sharing method based on blockchain and differential privacy Download PDFInfo
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Abstract
The application discloses a crowdsourcing platform statistical data on-demand sharing method based on block chains and differential privacy, which adopts the design of adding noise by adopting a differential privacy technology, can protect the privacy of user data and avoid the data from being leaked and maliciously utilized after being attacked by statistics. Meanwhile, the design of dispersing rights by adopting the dual roles of the noise adding party and the data holder is adopted, so that the supervision rights are weakened; the block chain technology, homomorphic encryption and zero knowledge proof technology are adopted to ensure the integrity and non-tamper property of the data, minimize the access set of the data, only allow a designated supervisor to hold a final secret key, thereby enhancing the security and the credibility of the data and ensuring the availability of the data; by adopting the intelligent contract technology, the automation and standardization of the data can be realized, so that the data compliance requirement is met; and classifying the keys by adopting a key list scheme, and identifying the encrypted statistical data through keywords, so that the key list scheme is reused.
Description
Technical Field
The application relates to the technical field of data sharing, in particular to a crowdsourcing platform statistical data on-demand sharing method based on blockchain and differential privacy.
Background
Nowadays, the requirements of each data analysis company on custom data of a specific industry are quite huge, the generation amount of the specific data is relatively small, only partial data is easy to be held by industry head companies and cannot be directly acquired, and the research of industry information through a crowdsourcing platform becomes a mainstream industry custom data collection mode.
Crowd sourcing refers to the practice of a company or organization to outsource work tasks performed by employees in the past to unspecified (and often large) mass volunteers in a free voluntary fashion. Crowd-sourced tasks are typically undertaken by individuals, but may also occur in the form of individual productions relying on open sources if tasks requiring multi-person collaboration are involved.
The concrete flow of crowdsourcing is presented by Feng Jianhong et al in review. The main participants of crowd sourcing include task requesters, and task completion people. They are linked together by a task. When a task requester intends to complete his task using crowdsourcing, the crowdsourcing needs to be used as follows. (1) a design task; (2) utilizing a crowdsourcing platform to issue tasks and waiting for answers; (3) rejecting or receiving the answer of the worker; (4) And finishing the task according to the answer arrangement result of the worker. While the main steps of using crowd sourcing by workers include: (1) searching for a task of interest; (2) receiving a task; (3) answering the task; (4) and submitting an answer.
Feng Jianhong et al summarized in an overview some of the problems that exist in current crowdsourcing scenarios: the validity of the data obtained by crowdsourcing cannot be determined, so that the quality of a task is affected; the task performer cannot well match the required task, and a mechanism for improving task recommendation aiming at the preference of the task performer is needed; the data security and the user privacy security of the crowdsourcing platform have no good solution at present, and each user cannot be ensured to have independent privacy aiming at microscopic complex tasks.
In view of the above problems, in research at home and abroad, one of the solutions is to use a distributed storage technology to construct a partially decentralised alliance chain, and implement a crowdsourcing process based on the alliance chain. Recording the issuing task and the completion data by utilizing the non-tamperable characteristic of the block chain; audit trail invalidation data sources based on traceability of blockchain; based on the admittance of the alliance chain, user authority management is carried out, and the reliability and the safety of the system are improved. In the research at home and abroad, a plurality of specific schemes are provided based on platforms such as Ethernet, super ledger block chain and the like to solve the problems of crowdsourcing scene data storage and sharing privacy protection.
Li Ming et al propose a public chain based decentralized crowdsourcing framework CrowdBC in which the task of the requester can be solved by a group of staff without relying on any third party trusted authority, the author builds a prototype on the Ethernet using a real dataset, confirming its feasibility and requiring only a very low transaction cost. The Zhu Saide presents an innovative hybrid blockchain crowdsourcing platform zkCrowd. zkCrowd integrates a hybrid blockchain structure and improves on the consensus protocol and blockchain architecture, and ensures the security of communication and the reliability of transaction verification by using the dual-consensus protocol and the dual-chain architecture. Xu xiaolone et al propose a blockchain driven crowdsourcing scheme BPCM that considers privacy preservation in a mobile environment. The scheme respectively adopts density-based noisy application spatial clustering and improved dynamic programming to cluster requesters and generate service strategies. On the basis, a simple addition weighting and multi-criterion decision method is adopted, and optimization selection is carried out among maximization of service time, increase of profit and reduction of energy consumption.
However, the above prior art has the following drawbacks:
1. the crowdsourcing task data of the user is usually processed by a plurality of systems in the platform, the links are long, the work is inefficient, the legal rights of the user as a personal data main body are not considered in the sharing of the data, and a data holder does not have good protection measures to ensure the safety and reliability of the data. Meanwhile, the compliance data sharing among institutions lacks supervision of a supervision department, and can cause the flooding of junk data. How to realize the secure and controllable sharing of the crowd-sourced task data of the user is also an important problem to consider.
2. When the common blockchain scheme is adopted by the crowdsourcing platform, the scheme of directly adding the supervisor also easily causes excessive concentration of the supervisor rights, the encryption of the blocks by the common encryption means also causes that some past data cannot be effectively used, and the attack on the statistical data issued after the data are collected also causes data leakage.
Disclosure of Invention
Aiming at the inherent supervision deficiency of the blockchain and the leakage problem of the data, the application provides an on-demand sharing method of the crowd-sourced platform statistical data based on the blockchain and the differential privacy, in the scheme, in order to ensure that the past encryption data can be multiplexed, a design for classifying secret keys is adopted; in order to ensure the usability of the data, adopting a zero knowledge proof technology and a supervision node to jointly restrict the design of the data; in order to prevent the attack to which the statistical data is easily subjected, a design of adding noise by adopting a differential privacy protection means is adopted; to attenuate regulatory rights concentrations, a design is employed in which the rights are dispersed by the dual roles of the noise adder and the data holder.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides a crowdsourcing platform statistical data on-demand sharing method based on blockchain and differential privacy, which comprises the following sharing processes:
task data submission process: a user of the crowdsourcing platform submits data by calling an intelligent contract interface, the homomorphic encryption module is used for encrypting the data before submitting the data, the homomorphic encryption module uses a homomorphic encryption algorithm to encrypt by using a key of a data holder and then encrypts by using a key of a privacy protector, and the data is submitted to an intelligent contract after being encrypted and is issued to a blockchain;
data aggregation and differential privacy protection process: the privacy protecting party extracts encrypted data submitted by a user through an intelligent contract interface, decrypts the data by using a combined secret key of the privacy protecting party before using the data, and calls a zero knowledge proof module to ensure the availability of the data; calling a data aggregation and differential privacy protection module after collecting data, adding differential privacy noise after homomorphism addition and aggregation of the data, and publishing the data to a blockchain;
the data distribution process is as required: the data holder records key value pairs of the key and the task type in progress of the user while releasing the key to the data task person, and records a corresponding key list; when the third party data requesting party requests the corresponding task data, the data holder performs statistics, extraction and delivery to the third party data requesting party according to the keywords.
Further, before data is submitted, system initialization is carried out, a Paillier homomorphic encryption algorithm is firstly used on a privacy protecting party and a data holder node to generate a public-private key pair of the homomorphic encryption algorithm, and the privacy protecting party is generated with a node public-private key pair (pk a ，sk a ) A public-private key pair (pk) corresponding to a certain class is generated for the data holder b ，sk b ) The data holder records the public and private key pair and the classification name on a local list at the same time; the public key pk a And pk b Broadcast to existing task presenter nodes in a networkAt the same time, notifying pk when new task submitter node is added a And pk b 。
Further, the public and private key pair generation method of the homomorphic encryption algorithm comprises the following steps:
firstly, randomly selecting large prime numbers p and q with similar lengths, satisfying gcd (pq, (p-1) (q-1))=1, calculating N=pq, and lambda=lcm (p-1, q-1), wherein gcd (') is a calculated maximum common factor, and lcm (') is a calculated minimum common multiple;
then randomly selectAnd satisfies gcd (L (g) λ mod N 2 ) N) =1, note μ= (L (g) λ modN 2 )) - 1 mod N, function->Let (N, g) be the node public key pair and (λ, μ) be the node private key pair.
Further, in the task data submitting process, user data is added and proved on the data through a local zero knowledge proof module, and homomorphic encryption is carried out by utilizing a Paillier algorithm.
Further, the validity of the data encrypted by using the Paillier algorithm is proved by combining the Pedersen algorithm, the Fujisaki algorithm and the Bulletproof algorithm, and the specific method is as follows:
first, define user u i Is v i The Paillier algorithm public key (N, g) of the data holder is fetched; taking outGenerator g of q-order subgroup 2 Random group element h in the group 2 Constitutes Fujisaki algorithm parameters (g 2 ，h 2 N); get->Generator g of q-order subgroup 3 Random group element h in the group 3 Constitute Pedersen algorithm parameters (g 3 ，h 3 P, q); definition:
then, a random security parameter k is generated by taking the range length of the data to be encrypted as a parameter lDefinition:
definition e=h (a, b, c, x, y, z), f=d+ev,
finally Enc the b (message)＝(a，b，c，x，y，z，f，r xe ，r ye ，r ze ) After packaging, encrypting by using a public key of a privacy protecting party to generate Enc a (Enc b (message)) and then transmitted to the smart contract.
Further, in the data aggregation and differential privacy protection process, the privacy protection party aggregates and adds differential privacy noise to the data, and the steps are as follows:
step one: the privacy protecting party receives the encryption public key Enc a (Enc b (message)) and then first use its own key sk a Decrypting the data to obtain
Enc b (message)＝(a，b，c，x，y，z，f，r xe ，r ye ，r ze )；
Step two: definition f is more than or equal to 0 and less than or equal to 2 l+2k+1 The range of data is verified using equation (3):
if the equation is satisfied, verify passObtaining user u i Is (are) encrypted data i ；
Step three: after the privacy protecting party collects a plurality of data passing through verification, the data are aggregated by utilizing homomorphic addition characteristics of a Paillier algorithm to obtain aggregated data; then using the formula (4) to add noise to the aggregate data to obtain aggregate noise data
Where γ is the random noise sample on the aggregate dataset.
Step four: the privacy preserving party will aggregate noise dataAnd issuing the intelligent contract to the blockchain and simultaneously calling back the intelligent contract.
Compared with the prior art, the application has the following beneficial effects:
the method can realize safe, standard, credible, traceable and accessible sharing of the statistical data on the crowdsourcing platform by adopting a differential privacy technology, an intelligent contract technology in a blockchain, a zero knowledge proof technology and a homomorphic encryption technology, thereby promoting more application scenes and data values. The contributions of the application mainly include:
1. privacy protection: by adopting the design of adding noise by adopting the differential privacy technology, the privacy of user data can be protected, and the data is prevented from being leaked and maliciously utilized after being attacked by statistics. Meanwhile, the design of dispersing rights by adopting the dual roles of the noise adding party and the data holder is adopted, so that the supervision rights are weakened.
2. Data confidence level: the block chain technology, homomorphic encryption and zero knowledge proof technology are adopted to ensure the integrity and non-tamper property of the data, minimize the access set of the data, only allow the appointed supervisor to hold the final secret key, thereby enhancing the security and the credibility of the data and ensuring the availability of the data.
3. Compliance with: by adopting the intelligent contract technology, the automation and standardization of the data can be realized, thereby meeting the data compliance requirement.
4. Reusability: and classifying the keys by adopting a key list scheme, and identifying the encrypted statistical data through keywords, so that the key list scheme is reused.
In summary, the cryptographic technology and the intelligent contract technology in the blockchain adopted by the application can solve the privacy and security problems encountered by the crowdsourcing platform in the data sharing process, improve the security and credibility of the data and promote more application scenes and data values.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a frame diagram of an on-demand sharing method for crowd-sourced platform statistics based on blockchain and differential privacy according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application provides a crowdsourcing platform statistical data on-demand sharing method based on blockchain and differential privacy, which is shown in figure 1, and relates to four roles, namely a task submitting user, a third party data requiring party, a data holder and a privacy protecting party. The third party data demander comprises a task publisher, legal organization and personnel conforming to an external shared access strategy.
The on-demand sharing method for the statistical data of the crowdsourcing platform comprises the following sharing processes:
task data submission: a user of the crowdsourcing platform submits data by calling an intelligent contract interface, the data are required to be encrypted before being submitted, a homomorphic encryption module is required to be used for encryption, a homomorphic encryption algorithm is required to be used for encryption with a secret key based on a combination of a data holder and a privacy protector, and the secret key of the data holder is firstly used for encryption and then the secret key of the privacy protector is used for encryption. And after the data is encrypted, submitting the encrypted data to an intelligent contract and issuing the encrypted data to a blockchain.
Data aggregation and differential privacy protection: the privacy protector extracts the encrypted data submitted by the user through the intelligent contract interface, the data is decrypted by using the combined key of the privacy protector before the data is used, and the decrypted data is called to a zero knowledge proof module, so that the usability of the data is ensured. And calling a data aggregation and differential privacy protection module after a certain amount of data is collected, adding differential privacy noise after homomorphism addition and aggregation of the data, and publishing the data to a blockchain.
Data distribution on demand: the data holder records the key value pair of the key and the task type in progress of the user while releasing the key to the data task person, and records the key value pair into a corresponding key list. When the third party data requesting party requests the corresponding task data, the data holder may extract the statistics based on the keywords and deliver to the third party data requesting party.
The sharing process is described in detail below. The symbols involved in the schemes are described in the following table:
table 1 symbology and description
(1) System initialization algorithm:
in the system initialization process, a public-private key pair of a homomorphic encryption algorithm needs to be generated on a privacy protecting party and a data holder node in the system, and the scheme is generated by using a Paillier homomorphic encryption algorithm. First, large prime numbers p, q of similar length are randomly selected and gcd (pq, (p-1) (q-1))=1 is satisfied, n=pq, λ=lcm (p-1, q-1) is calculated, gcd (j) is the calculated maximum common factor, lcm (j) is the calculated minimum common multiple.
Randomly selectAnd satisfies gc d (L (g) λ mod N 2 ) N) =1, note μ= (L (g) λ modN 2 )) -1 mod N, function->Let (N, g) be the node public key pair and (λ, μ) be the node private key pair.
A system initialization algorithm is applied at the privacy preserving party and the data holder node respectively, and a node public-private key pair (pk) is generated for the privacy preserving party a ，sk a ) A public-private key pair (pk) corresponding to a certain class is generated for the data holder b ，sk b ) The data holder records the public-private key pair and the class name simultaneously on the local list. Respectively the public keys pk a And pk b Broadcast to existing task presenter nodes in the network while notifying pk when a new task presenter node joins a And pk b 。
(2) Task data submission algorithm:
the user data is added with a certificate on the data through a local zero knowledge proof module, and homomorphic encryption is carried out by utilizing a Paillier algorithm. Because the encrypted data needs to be subjected to certification generation so that a privacy protecting party can prove the validity of the data under the condition of not contacting the original data, the scheme combines Pedersen commitment, fujisaki commitment and Bulletproof algorithm to realize the certification of the validity of the encrypted data by using Paillier algorithm.
First, define user u i Is v i The Paillier algorithm public key (N, g) of the data holder is taken. Taking outGenerator g of q-order subgroup 2 Random group element h in the group 2 Constitutes Fujisaki algorithm parameters (g 2 ，h 2 N). Get->Generator g of q-order subgroup 3 Random group element h in the group 3 Constitute Pedersen algorithm parameters (g 3 ，h 3 P, q). Defined by equation (1):
then the range length of the data to be encrypted is taken as a parameter l, and a random security parameter k is generatedDefined by equation (2):
definition e=h (a, b, c, x, y, z), enc Enc b (message)＝(a，b，c，x，y，z，f，r xe ，r ye ，r ze ) After packaging, encrypting by using a public key of a privacy protecting party to generate Enc a (Enc b (message)) and then transmitted to the smart contract.
(3) Data aggregation and difference algorithm:
the privacy protecting party performs aggregation and differential privacy noise addition on the data, and the steps are as follows:
step one: the privacy protecting party receives Enc a (Enc b (message)) and then first use its own key sk a Decrypting the data to obtain Enc b (message)＝(a，b，c，x，y，z，f，r xe ，r ye ，r ze )。
Step two: definition f is more than or equal to 0 and less than or equal to 2 l+2k+1 The range of data is verified using equation (3):
if the equation is satisfied, the verification is passed, and the user u is obtained i Is (are) encrypted data i 。
Step three: after the privacy protecting party collects a plurality of data passing through verification, the data are aggregated by utilizing homomorphism addition characteristics of the Paillier algorithm to obtain aggregated data. Adding noise to the aggregate data to obtain aggregate noise data by using the formula 4-4
Where γ is the random noise sample on the aggregate dataset.
Step four: the privacy preserving party will aggregate noise dataAnd issuing the intelligent contract to the blockchain and simultaneously calling back the intelligent contract.
(4) Data extraction algorithm:
the data holder classifies the aggregate noise data stored on the chain according to the public-private key pairs recorded in the list. Under the condition that the third party data demand party requests the data demand, the corresponding data are decrypted and distributed to the third party data demand party.
The application has the following advantages:
1. data stream confidentiality: under a traditional crowdsourcing platform, a main node through which data passes in the process of circulation is a centralized platform node. Data is generally in a plaintext state when shared on the node, and subsequent data flow is not supervised, so that data leakage is easily caused when the centralized node is attacked. The blockchain platform proposed to improve the centralization problem shares data in plaintext form to the blockchain, and the data still lacks confidentiality. The application introduces two supervisors with partial rights, encrypts data twice in the form of a combined secret key, and when the data flows from a task submitting party to a privacy protecting party, the data is encrypted by a public key pair of a data holding party, and the data cannot be analyzed from the perspective of the data holding party. When the privacy protecting party is attacked to cause data leakage, the attacker cannot obtain the user data in the clear. When data flows from the privacy preserving party to the data holding party, the data presents the state of statistical data. From the perspective of the data holder, the real task data of the user cannot be directly obtained through the statistical data. The third party data demander only communicates with the data holder and cannot directly join the network, and compared with the traditional blockchain platform, the application realizes confidentiality of data.
2. Statistical aggressiveness of data: in the on-demand sharing scheme of the crowd-sourced platform statistical data, when the data flows to a third party data requiring party, the data presents aggregated statistical data. From the visual point of view, personal data cannot be analyzed through statistical data, and the personal data privacy is protected to a certain extent. However, by means of differential attack of the data, the user data within the collection can be analyzed from several statistics. Therefore, the application adopts differential privacy technology, and noise in the data is controlled within a certain privacy budget by introducing noise, so that the data still has statistical usability, the possibility of differential attack of the data is eliminated, and the data has statistical attack resistance in the query process conforming to the format.
3. Flow transparency: the traditional crowdsourcing platform mainly operates by a centralized platform, the execution flow is opaque, and whether other operations exist on the path from the user to the demander can not be known. The application discloses the code of the intelligent contract by adopting the intelligent contract technology based on the blockchain, and any role can check and verify the function and the executing process of the contract. This means that all participants can understand the specific content of the contract, avoiding untrustworthy and disputes due to the opaque nature of the contract. Second, the execution of the smart contract is published and recorded on the blockchain. The block chain is a decentralized distributed database, all nodes store the same data, and each block contains the hash value of the previous block, so that the data is ensured not to be tampered. The distributed recording mode ensures that the execution process of the contract is not interfered by any centralization mechanism, and can also prevent the data from being tampered or deleted. Finally, the execution results of the smart contract are also disclosed, and the execution results are also recorded on the blockchain. Each node can verify the execution result of the intelligent contract and agree through a consensus algorithm. This way of disclosure and consensus can prevent either party from tampering with the execution results, or can prevent the execution results from being hidden or deleted. Thereby realizing flow transparency.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may be modified or some technical features may be replaced with others, which may not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (6)
1. The on-demand sharing method for the statistical data of the crowdsourcing platform based on the blockchain and the differential privacy is characterized by comprising the following sharing processes:
task data submission process: a user of the crowdsourcing platform submits data by calling an intelligent contract interface, the homomorphic encryption module is used for encrypting the data before submitting the data, the homomorphic encryption module uses a homomorphic encryption algorithm to encrypt by using a key of a data holder and then encrypts by using a key of a privacy protector, and the data is submitted to an intelligent contract after being encrypted and is issued to a blockchain;
data aggregation and differential privacy protection process: the privacy protecting party extracts encrypted data submitted by a user through an intelligent contract interface, decrypts the data by using a combined secret key of the privacy protecting party before using the data, and calls a zero knowledge proof module to ensure the availability of the data; calling a data aggregation and differential privacy protection module after collecting data, adding differential privacy noise after homomorphism addition and aggregation of the data, and publishing the data to a blockchain;
the data distribution process is as required: the data holder records key value pairs of the key and the task type in progress of the user while releasing the key to the data task person, and records a corresponding key list; when the third party data requesting party requests the corresponding task data, the data holder performs statistics, extraction and delivery to the third party data requesting party according to the keywords.
2. The method of claim 1, wherein prior to data submission, performing system initialization, generating a public-private key pair of a peer encryption algorithm on a privacy protecting party and a data holder node using a Paillier peer encryption algorithm, and generating a node public-private key pair (pk) for the privacy protecting party a ，sk a ) A public-private key pair (pk) corresponding to a certain class is generated for the data holder b ，sk b ) The data holder records the public and private key pair and the classification name on a local list at the same time; the public key pk a And pk b Broadcast to existing task presenter nodes in the network while notifying pk when a new task presenter node joins a And pk b 。
3. The method for sharing the crowd-sourced platform statistical data on demand based on blockchain and differential privacy according to claim 2, wherein the public-private key pair generation method of the homomorphic encryption algorithm is as follows:
firstly, randomly selecting large prime numbers p and q with similar lengths, satisfying gcd (pq, (p-1) (q-1))=1, calculating N=pq, and lambda=lcm (p-1, q-1), wherein gcd (') is a calculated maximum common factor, and lcm (') is a calculated minimum common multiple;
then randomly selectAnd satisfies gcd (L (g) λ modN 2 ) N) =1, note μ= (L (g) λ modN 2 )) -1 mod N, function->Let (N, g) be the node public key pair and (λ, μ) be the node private key pair.
4. The method for on-demand sharing of crowd-sourced platform statistics based on blockchains and differential privacy according to claim 1, wherein in the task data submission process, user data is first added and proved on the data through a local zero knowledge proof module, and homomorphic encryption is performed by utilizing a Paillier algorithm.
5. The method for sharing the statistical data of the crowdsourcing platform based on the blockchain and the differential privacy according to claim 4, wherein the proving of the validity of the data encrypted by using the Paillier algorithm is realized by combining a Pedersen algorithm, a Fujisai algorithm and a Bulletproof algorithm, and the specific method is as follows:
first, define user u i Is v i The Paillier algorithm public key (N, g) of the data holder is fetched; taking outGenerator g of q-order subgroup 2 Random group element h in the group 2 Constitutes Fujisaki algorithm parameters (g 2 ，h 2 N); get->Generator g of q-order subgroup 3 Random group element h in the group 3 Constitute Pedersen algorithm parameters (g 3 ，h 3 P, q); definition:
then, a random security parameter k is generated by taking the range length of the data to be encrypted as a parameter lDefinition:
definition e=h (a, b, c, x, y, z), f=d+ev,r ye ＝e·r 2 +r y ，r ze ＝e·r 3 +r z ；
finally Enc the b (message)＝(a，b，c，x，y，z，f，r xe ，r ye ，r ze ) After packaging, encrypting by using a public key of a privacy protecting party to generate Enc a (Enc b (message)) and then transmitted to the smart contract.
6. The method for on-demand sharing of crowd-sourced platform statistics based on blockchain and differential privacy according to claim 1, wherein in the data aggregation and differential privacy protection process, the privacy protection party aggregates and differential privacy noise adds data, and the steps are as follows:
step one: the privacy protecting party receives the encryption public key Enc a (Enc b (message)) and then first use its own key sk a Decrypting the data to obtain Enc b (message)＝(a，b，c，x，y，z，f，r xe ，r ye ，r ze )；
Step two: definition f is more than or equal to 0 and less than or equal to 2 l+2k+1 The range of data is verified using equation (3):
if the equation is satisfied, the verification is passed, and the user u is obtained i Is (are) encrypted data i ；
Step three: after the privacy protecting party collects a plurality of data passing through verification, the data are aggregated by utilizing homomorphic addition characteristics of a Paillier algorithm to obtain aggregated data; then using the formula (4) to add noise to the aggregate data to obtain aggregate noise data
Where γ is the random noise sample on the aggregate dataset.
Step four: the privacy preserving party will aggregate noise dataAnd issuing the intelligent contract to the blockchain and simultaneously calling back the intelligent contract.
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