A public integrity audit method for multi-value density cloud storage data

Abstract

The present invention discloses a method for public auditing of the integrity of multi-value density cloud storage data, which relates to the field of information security and includes the following steps: parameter preprocessing step; user key generation step; user data preprocessing step; cloud data preprocessing step; challenge step; evidence generation step; and evidence verification step. The public auditing method proposed in the present invention not only ensures the safe and efficient auditing of the integrity of multi-value density data in a cloud storage environment, but also supports strength adaptive auditing of multi-value density data and realizes privacy protection of cloud data value attributes.

Description

Technical Field

[0001] The present invention belongs to the field of information security, and in particular relates to data security auditing for cloud storage, and is suitable for public integrity auditing of multi-value density data in a cloud storage environment. Background Art

[0002] In today's era of digital information explosion, the amount of data generated and processed on the Internet is growing exponentially. This rapid growth has brought unprecedented challenges to the storage and management of massive data. Traditional storage methods often cannot meet the needs of modern applications in terms of scalability, availability, and cost-effectiveness. In order to meet these challenges, cloud storage services have emerged, providing developers and enterprises with a variety of global and persistent storage solutions. It can store users' local data in the online storage space provided by CSP through the network. Although it has considerable advantages, it is undeniable that cloud storage also brings challenges to data integrity and security. Therefore, it is crucial to regularly verify the security of outsourced data. To solve this problem, cloud data auditing technology has been widely proposed. Its purpose is to remotely verify the integrity of outsourced data.

[0003] However, when such a large amount of data is migrated to cloud storage, if the third-party auditor performs indiscriminate integrity verification tasks on all cloud data, two problems will arise. (1) User demand. For enterprises and individuals, high-value data often carries greater risks and requires more frequent audits to ensure its security and integrity. (2) Waste of resources. Performing indiscriminate audit tasks on a large amount of data may cause users to pay a huge fee to the third-party auditor, and it will also lead to a waste of computing resources. Therefore, performing audit tasks of different intensities for data according to its value level is a key challenge.

[0004] In order to solve the above problems, some researchers adopt different audit schemes for different files by using the idea of ​​decision-making method. Wang Huifeng et al. proposed an adaptive data possession proof method, which dynamically adjusts the audit scheme of files according to file attributes and user needs, so that the audit requirements of files and the execution intensity of audit schemes are highly matched (see Wang Huifeng, Li Zhanhuai, Zhang Xiao et al. Adaptive Audit Method for Data Integrity in Cloud Storage [J]. Computer Research and Development, 2017, 54(01): 172-183.). Although this scheme can improve audit efficiency, the practice of directly informing auditors of file attributes will also lead to file privacy leakage, which may make malicious users more motivated and targeted to obtain or attack the required files. On this basis, in order to achieve privacy protection of file value, Huang et al. proposed a privacy-preserving certificateless public auditing scheme supporting different auditing frequencies, allowing TPA to complete the audit tasks of high- and low-value files at different audit frequencies (Huang Y, Shen W, Qin J, et al. Privacy-preserving certificateless public auditing supporting different auditing frequencies [J]. Computers & Security, 2023, 128: 103181). The scheme divides files into two categories and performs audit tasks for each category separately. At the same time, in order to prevent the third-party audit end from identifying high-value files, the permutation technology is used to confuse the actual file index. However, the scheme also has certain limitations: it only divides files into two categories, lacks flexibility when processing new files that need to regenerate the file sorting index table, and cannot effectively protect the privacy of file value. In addition, the permutation technology is easy to be cracked, and once it is leaked, there is a risk of exposing the value of all files. Therefore, it is very necessary and valuable to achieve public audits of different intensities for different user needs and data values ​​under the premise of protecting user privacy. Summary of the invention

[0005] The purpose of the present invention is to address the defects and shortcomings in the prior art and propose a public integrity audit method for multi-value density cloud storage data. While ensuring the security and efficient audit of cloud data, it can also achieve adaptive strength auditing of multi-value density data, while ensuring the privacy protection of the value attributes of cloud data (that is, the third-party audit end can audit data of different values ​​at different frequencies in the audit task, and the third-party audit end cannot understand information about the value of the data).

[0006] The technical solution of the present invention is as follows.

[0007] A method for public integrity audit of multi-value density cloud storage data, including:

[0008] S1, parameter preprocessing step: obtain two pre-selected large prime numbers p and q, and the multiplication cyclic group Z of p p * The multiplicative cyclic group Z of q q * and the multiplicative cyclic group Z p * The generator g of Z is defined as a mapping of a string of arbitrary length to Z q * The chameleon hash function CH is a secure encryption function of the elements in, and the private key of the chameleon hash function is shared by the user and the CSP; the public parameters {p, q, Z p * ,Z q * ,g,CH};

[0009] S2, user key generation step: the user generates a key pair, saves the private key, and publishes the public key to the CSP and the third-party audit end; the private key is the multiplication cyclic group Z q * The first random number in; the public key belongs to the multiplicative cyclic group Z p * ;

[0010] S3, user data preprocessing step: the user first generates a file identifier for the file to be processed; divides the file into multiple data blocks, and selects a secret random value for the file, the secret random value being a multiplication cyclic group Z q * The second random number in the data block; the value information of each data block is recorded at the same time to obtain a data block value information set; for each data block, the user needs to select a random chameleon hash auxiliary value for it; according to the user private key, the securely encrypted chameleon hash function and the chameleon hash auxiliary value, the user generates a corresponding data block label for each data block and generates a data block label set; then, the user and the CSP agree on the generation rules of the data index table; finally, the user sends the file data block, the data block signature set, the chameleon hash auxiliary value set, the data block value information set and the generation rules to the CSP;

[0011] S4, cloud data preprocessing step: after receiving the file data block, data block signature set, chameleon hash auxiliary value set, data block value information set and generation rules sent by the user, the CSP performs correctness verification processing on the data block and its corresponding data block label according to the user's public key. When the verification is correct, the user is informed that the user can delete the local file data block and data block label set; then, the CSP uses the data block value information set and generates a data index table according to the generation rules agreed with the user; in addition, according to the chameleon hash auxiliary value set, new chameleon hash auxiliary values ​​are calculated for all the expanded block indexes in the data index table to obtain a new chameleon hash auxiliary value set;

[0012] S5, challenge step: when the user needs to verify the data integrity, the user sends an audit request to the third-party audit terminal. After receiving the request, the third-party audit terminal generates a challenge index set and a random number set, obtains a challenge set based on the challenge index set and the random number set, and sends the challenge set to the CSP;

[0013] S6, evidence generation step: After receiving the challenge set sent by the third-party audit end, the CSP calculates the challenged data block, the data proof, label proof, auxiliary proof parameters, new chameleon hash auxiliary value set and original data block proof parameters of the challenge data block according to the challenge index set and the data index table in the challenge set; then, the CSP generates a proof set with the data proof, label proof, auxiliary proof parameters, new chameleon hash auxiliary value set and original data block proof parameters, and sends the proof set to the third-party audit end;

[0014] S7, evidence verification step: After receiving the proof set, the third-party audit end performs data integrity verification processing, generates an integrity verification result, and sends the integrity verification result to the user.

[0015] Preferably, the parameter preprocessing step specifically includes:

[0016] S11, obtain the multiplication cyclic group Z of two pre-selected large prime numbers p and q, p p * The multiplicative cyclic group Z of q q * and the multiplicative cyclic group Z p * The generator g of ; where p and q satisfy the relationship q|(p-1), and the order of g is a prime number q;

[0017] S12, define a secure cryptographic chameleon hash function CH:{0,1}*→Z q *, where the role of the secure cryptographic chameleon hash function CH is to map a string of any length {0,1}* to a multiplicative cyclic group Z q * In the elements, let sk ch The private key of the Chameleon hash function CH shared by the user and CSP;

[0018] S13, public system parameters {p,q,Z p * ,Z q * ,g,CH}.

[0019] Preferably, the user key generation step specifically includes:

[0020] S21, the user generates a key pair, namely the private key sk U =s, public key pk U =y; where s∈Z q * A random number selected by the user.

[0021] y=g s ∈Z p * ;

[0022] S22, the user saves the private key sk U , and the public key pk U Published to CSP and third-party auditors.

[0023] Preferably, the user data preprocessing step specifically includes:

[0024] S31, the user first generates a file identifier name for the file F to be processed, and divides the file F into n data blocks to obtain file data block F = {m i |1≤i≤n}; where m i For the i-th data block, randomly select α∈Z q * As a secret random value, calculate u = g α ∈Z p * , and make u public;

[0025] S32, record the value information of each data block to obtain the data block value information set V = {V i |1≤i≤n}; where V i is the data block m i The value level of

[0026] S33, for each data block m i , the user selects a random chameleon hash auxiliary value k for iti , get the Chameleon Hash auxiliary value set K = {k i |1≤i≤n}, and calculate the corresponding

[0027] S34, the user provides each data block m i Generate the corresponding data block label σ i , get the data block label set Φ, the calculation formula is:

[0028] σ i =sk U ·CH(name||i,k i )+α·m i -k i ·r i ;

[0029] Among them, σ i is the data block label of the i-th data block, name is the file identifier, and the data block label set Φ = {σ i |1≤i≤n};

[0030] S35, then, the user and CSP agree on the generation rule R of the data index table DIT, that is, how to extend the index for each data block according to its value level; assuming that data block m i The value level is V i , CSP adds V to this data block i-1 indexes, there are a total of V i An index points to the data block; after the indexes of all data blocks are expanded, the final data index table DIT is obtained;

[0031] S36, finally, the user sends the file data block F, the data block signature set Φ, the chameleon hash auxiliary value set K, the data block value information set V and the generation rule R to the CSP.

[0032] Preferably, the cloud data preprocessing step specifically includes:

[0033] S41, after receiving the file data block F, data block signature set Φ, chameleon hash auxiliary value set K, data block value information set V and generation rule R sent by the user, the CSP verifies the correctness of the data block and its corresponding data block label according to the user's public key. When the verification is correct, the user is informed, and the user can delete the local data block and the data block label set;

[0034] The verification formula for the correctness verification process is:

[0035]

[0036] Among them, r i According to the Chameleon Hash auxiliary value k i The calculation formula is:

[0037]

[0038] S42, then, the CSP needs to generate a data index table DIT using the data block value information set V and a data index table DIT generation rule R agreed with the user;

[0039] The mapping relationship between the expanded block index and the original data block index in the data index table DIT is denoted as f;

[0040] S43, in addition, using the chameleon hash auxiliary value set K for all the extended block indexes in the data index table DIT, new chameleon hash auxiliary values ​​k are calculated respectively. j ', get a new chameleon hash auxiliary value set K' = {k j '|1≤j≤m}, where m is the number of indexes in the data index table;

[0041] Among them, the original chameleon hash value corresponding to the current block and the newly calculated chameleon hash value satisfy the following relationship:

[0042] CH(name||i,k i )=CH(name||j,k′ j );

[0043] Therefore, the new Chameleon Hash auxiliary value calculation formula is as follows:

[0044]

[0045] Among them, sk ch The private key of the Chameleon hash function that the user shares with the CSP.

[0046] Preferably, the challenge step specifically includes:

[0047] S51, when the user needs to verify the data integrity, an audit request is sent to the third-party audit terminal;

[0048] S52, after receiving the request, the third-party audit terminal generates a challenge index set Ψ and a random number set S. First, a challenge index set Ψ containing c elements is generated. l |1≤l≤c,c≤n}; where c is the number of challenge blocks, ψ l is the index number of the lth challenge block;

[0049] S53, then generate a corresponding random number s for each challenge index l , get the random number set S = {s l |1≤l≤c}; where s l Z q * The third random number in ;

[0050] S54, the third-party audit end obtains a challenge set Chal={Ψ,S} according to the challenge index set Ψ and the random number set S, and sends the challenge set Chal to the CSP.

[0051] Preferably, the evidence generation step specifically includes:

[0052] S61, after the CSP receives the challenge set Chal sent by the third-party audit terminal, the challenge index set Ψ and the random number set S in the challenge set Chal are calculated according to the data index table DIT to generate the data proof μ of the challenge data block, and the calculation formula is:

[0053]

[0054] in, Indicates that the original data index is data block; x is a random mask used to protect data privacy;

[0055] S62, according to the data index table DIT, the challenge index set Ψ and the random number set S in the challenge set Chal are calculated to generate the corresponding label proof σ, and the calculation formula is:

[0056]

[0057] in, The original data index is The label corresponding to the data block;

[0058] S63, calculating the auxiliary proof parameter Q according to the challenge index set Ψ and the random number set S in the challenge set Chal, the calculation formula is:

[0059]

[0060] S64, according to the data index table DIT and the challenge set Chal, the original data block proof parameter r is calculated, and the calculation formula is:

[0061]

[0062] in, The Chameleon Hash auxiliary value can be The calculation formula is:

[0063] S65, generate all proof sets Proof = {μ, σ, Q, K', r}, and send the proof set to a third-party audit terminal; wherein K' is a new chameleon hash auxiliary value set generated by the CSP.

[0064] Preferably, the evidence verification step specifically includes:

[0065] S71, after the third-party audit end receives the proof set Proof, it performs data integrity verification processing, and the integrity verification formula is:

[0066]

[0067] S72, when it is determined that the integrity verification formula is established, a CSP correctly stores data successfully signal is generated, and the CSP correctly stores data successfully signal is sent to the user;

[0068] S73: When it is determined that the integrity verification formula is not established, a CSP correctly storing data failure signal is generated, and the CSP correctly storing data failure signal is sent to the user.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention provides a public integrity audit method for multi-value density cloud storage data, including a parameter preprocessing step, a user key generation step, a user data preprocessing step, a cloud data preprocessing step, a challenge step, an evidence generation step and an evidence verification step. It can ensure the safe and efficient audit of the integrity of multi-value density data in a cloud storage environment, and support adaptive strength auditing of multi-value density data and privacy protection of cloud data value attributes. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 A flowchart of a method for public auditing integrity of multi-value density cloud storage data according to an embodiment of the present invention;

[0073] Figure 2An interactive flow chart of a method for public auditing integrity of multi-value density cloud storage data according to an embodiment of the present invention;

[0074] Figure 3 A data index table diagram is provided for the integrity public audit method for multi-value density cloud storage data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] See also Figure 1 and Figure 2 , an embodiment of the present invention discloses a method for public integrity audit of multi-value density cloud storage data, including a parameter preprocessing step, a user key generation step, a user data preprocessing step, a cloud data preprocessing step, a challenge step, an evidence generation step and an evidence verification step, as follows.

[0077] S1, parameter preprocessing step: obtain two pre-selected large prime numbers and two multiplication cyclic groups with orders of the large prime numbers, define a securely encrypted chameleon hash function, whose private key is shared by the user and the cloud service provider (Cloud Service Provider, CSP), and disclose system parameters.

[0078] The specific implementation is as follows.

[0079] S11, first, obtain two pre-selected large prime numbers p and q, and two multiplication cyclic groups Z of order p and q respectively. p * and Z q * , the multiplicative cyclic group Z p * The generator g of ; where p and q satisfy the relationship q|(p-1), and the order of g is a prime number q;

[0080] S12, then, define a cryptographically secure chameleon hash function CH:{0,1}*→Z q * , where the role of the secure cryptographic chameleon hash function CH is to map a string of any length {0,1}* to a multiplicative cyclic group Z q * In the elements, let sk chIt is the private key of the Chameleon hash function CH shared by the user and CSP.

[0081] S13, finally, the system parameters {p,q,Z p * ,Z q * ,g,CH}.

[0082] S2, user key generation step: the user generates a key pair, saves the private key, and publishes the public key to the CSP and the third-party audit end; the private key is a multiplication cyclic group The first random number in ; the public key belongs to the multiplicative cyclic group

[0083] The specific implementation is as follows.

[0084] S21, the user generates a key pair, namely the private key sk U =s, public key pk U =y; where s∈Z q * The first random number y=g selected by the user s ∈Z p * .

[0085] S22, the user saves the private key sk U , and the public key pk U Published to CSP and third-party auditors.

[0086] S3, user data preprocessing step: the user first generates a file identifier for the file to be processed; divides the file into multiple data blocks, and selects a secret random value for the file; records the value information of each data block at the same time to obtain a data block value information set; for each data block, the user needs to select a random chameleon hash auxiliary value for it; based on the user private key, the securely encrypted chameleon hash function, and the chameleon hash auxiliary value, the user generates a corresponding data block label for each data block and generates a data block label set; then, the user and the CSP agree on the generation rules of the data index table, that is, how to expand the number of indexes for each data block according to its value level; finally, the user sends the file data block, the data block signature set, the chameleon hash auxiliary value set, the data block value information set, and the generation rules to the CSP.

[0087] The specific implementation is as follows.

[0088] S31, the user first generates a file identifier name for the file F to be processed, and divides the file F into n data blocks to obtain file data block F = {m i |1≤i≤n}; where m iFor the i-th data block, randomly select α∈Z q * As a secret random value, calculate u = g α ∈Z p * , and make u public.

[0089] S32, record the value information of each data block to obtain the data block value information set V = {V i |1≤i≤n}, where V i (i=1,2,…,n) is data block m i value level.

[0090] S33, for each data block m i , the user selects a random chameleon hash auxiliary value k for it i , get the Chameleon Hash auxiliary value set K = {k i |1≤i≤n}, and calculate the corresponding

[0091] S34, the user provides each data block m i Generate the corresponding data block label σ i , get the data block label set Φ, the calculation formula is:

[0092] σ i =sk U ·CH(name||i,k i )+α·m i -k i ·r i ;

[0093] Among them, σ i is the data block label of the i-th data block, name is the file identifier, i is the index of the data block, and the data block label set Φ = {σ i |1≤i≤n}.

[0094] S35, then, the user and CSP agree on the generation rule R of the data index table DIT, that is, how to extend the index for each data block according to its value level; assuming that data block m i The value level is V i , CSP adds V to this data block i-1 indexes, there are a total of V i An index points to the data block; after the indexes after the expansion of all data blocks are aggregated, the final data index table DIT is obtained.

[0095] S36, finally, the user sends the file data block F, the data block signature set Φ, the chameleon hash auxiliary value set K, the data block value information set V and the generation rule R to the CSP.

[0096] S4, cloud data preprocessing step: after receiving the file data block, the data block signature set, the chameleon hash auxiliary value set, the data block value information set and the generation rule sent by the user, the CSP verifies the correctness of the data block and its corresponding data block label according to the user public key. When the verification is correct, the user is informed, and the user can delete the local data block and the data block label set; then, the CSP needs to use the data block value information set and generate a data index table according to the generation rule agreed with the user; in addition, according to the chameleon hash auxiliary value set, new chameleon hash auxiliary values ​​are calculated for all the expanded block indexes in the data index table to obtain a new chameleon hash auxiliary value set.

[0097] The specific implementation is as follows.

[0098] S41, after receiving the file data block F, data block signature set Φ, chameleon hash auxiliary value set K, data block value information set V and generation rule R sent by the user, the CSP verifies the correctness of the data block and its corresponding data block label according to the user's public key. When the verification is correct, the user is informed and the user can delete the local data block and the data block label set to save storage space.

[0099] The verification formula for the correctness verification process is:

[0100]

[0101] Among them, r i According to the Chameleon Hash auxiliary value k i The calculation formula is:

[0102]

[0103] S42, then, the CSP needs to use the data block value information set V and the data index table DIT generation rule R agreed with the user to generate the data index table DIT, see Figure 3 As shown in the figure, it is essentially to express the mapping relationship between the generated extended block index and the original data block index. Four data block levels are defined in the figure. Suppose the user uploads a file containing n data blocks, that is, {m i |1≤i≤n}, their value levels are {V i|1≤i≤n}, where the first data block m1 has a value level of 2, then the generated data index table DIT contains two indexes pointing to m1; the second data block m2 has a value level of 1, then the generated data index table DIT contains an index pointing to m2; the i-th data block m i The value level is 3, then the generated data index table DIT contains three pointers to m i Index to the nth data block m n If the value level is n, then the generated data index table DIT contains n pointers to m n wherein the mapping relationship between the expanded block index and the original data block index in the data index table DIT is recorded as

[0104] S43, in addition, using the chameleon hash auxiliary value set K for all the extended block indexes in the data index table DIT, new chameleon hash auxiliary values ​​k are calculated respectively. j ', get a new chameleon hash auxiliary value set K' = {k j '|1≤j≤m}, where m is the number of indexes in the data index table.

[0105] Among them, the original chameleon hash value corresponding to the current block and the newly calculated chameleon hash value satisfy the following relationship:

[0106] CH(name||i,k i )=CH(name||j,k′ j );

[0107] Therefore, the new Chameleon Hash auxiliary value calculation formula is as follows:

[0108]

[0109] Among them, sk ch The private key of the Chameleon hash function that the user shares with the CSP.

[0110] S5, challenge step: when the user needs to verify the data integrity, an audit request is sent to the third-party audit end. After receiving the request, the third-party audit end generates a challenge index set and a random number set, obtains a challenge set based on the challenge index set and the random number set, and sends the challenge set to the CSP.

[0111] The specific implementation is as follows.

[0112] S51, when the user needs to verify the data integrity, an audit request is sent to the third-party audit terminal.

[0113] S52, after receiving the request, the third-party audit terminal generates a challenge index set Ψ and a random number set S. First, a challenge index set Ψ containing c elements is generated. l |1≤l≤c,c≤n}; where c is the number of challenge blocks, ψ l is the index number of the lth challenge block.

[0114] S53, then generate a corresponding random number s for each challenge index l , get the random number set S = {s l |1≤l≤c}; where s l Z q * The third random number in .

[0115] S54, the third-party audit end obtains a challenge set Chal={Ψ,S} according to the challenge index set Ψ and the random number set S, and sends the challenge set Chal to the CSP.

[0116] S6, evidence generation step: after the CSP receives the challenge set sent by the third-party audit end, it determines the challenged data block according to the challenge index set in the challenge set and the data index table, and calculates the data proof, label proof and auxiliary proof parameters of the challenge data block; then, the CSP generates a proof set with the data proof, the label proof, the auxiliary proof parameters and the new chameleon hash auxiliary value set, and sends the proof set to the third-party audit end.

[0117] The specific implementation is as follows.

[0118] S61, after the CSP receives the challenge set Chal sent by the third-party audit terminal, the challenge index set Ψ and the random number set S in the challenge set Chal are calculated according to the data index table DIT to generate the data proof μ of the challenge data block, and the calculation formula is:

[0119]

[0120] in, Indicates that the original data index is data block; x is a random mask used to protect data privacy.

[0121] S62, according to the data index table DIT, the challenge index set Ψ and the random number set S in the challenge set Chal are calculated to generate the corresponding label proof σ, and the calculation formula is:

[0122]

[0123] in, The original data index is The label corresponding to the data block.

[0124] S63, calculating the auxiliary proof parameter Q according to the challenge index set Ψ and the random number set S in the challenge set Chal, the calculation formula is:

[0125]

[0126] S64, according to the data index table DIT and the challenge set Chal, the original data block proof parameter r is calculated, and the calculation formula is:

[0127]

[0128] in, According to the Chameleon Hash auxiliary value The calculation formula is:

[0129] S65, generate all proof sets Proof = {μ, σ, Q, K', r}, and send the proof set to a third-party audit terminal; wherein K' is a new chameleon hash auxiliary value set generated by the CSP.

[0130] S7, evidence verification step: After receiving the proof set, the third-party audit end performs data integrity verification processing, generates an integrity verification result, and sends the integrity verification result to the user.

[0131] The specific implementation is as follows.

[0132] S71, after the third-party audit end receives the proof set Proof, it performs data integrity verification processing, and the integrity verification formula is:

[0133]

[0134] S72, when it is determined that the integrity verification formula holds true, a CSP correctly stores data successfully signal is generated, and the CSP correctly stores data successfully signal is sent to the user.

[0135] S73: When it is determined that the integrity verification formula is not established, a CSP correctly storing data failure signal is generated, and the CSP correctly storing data failure signal is sent to the user.

[0136] In this embodiment, after the third-party audit terminal receives the proof set Proof, it performs data integrity verification. When it is determined that the integrity verification formula is established, it indicates that the CSP has correctly stored the data; when it is determined that the integrity verification formula is not established, it indicates that the stored data has been damaged, and the audit result is truthfully notified to the user.

[0137] In summary, the integrity public audit method for multi-value density cloud storage data can support adaptive strength auditing of multi-value density data, realize privacy protection of cloud data value attributes, and ensure higher security of the solution.

[0138] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention.

Claims

1. A method for public integrity audit of multi-value density cloud storage data, characterized in that: include: S1, parameter preprocessing step: obtain two pre-selected large prime numbers p and q, and the multiplication cyclic group Z of p p * , the multiplicative cyclic group Z of q q * and Z p * The generator g of Z is defined as a mapping of a string of arbitrary length to Z q * The chameleon hash function CH is a secure encryption function of the elements in, wherein the private key of the chameleon hash function is shared by the user and the cloud service provider CSP; the public parameters {p, q, Z p * ,Z q * ,g,CH}; S2, user key generation step: the user generates a key pair, saves the private key, and publishes the public key to the CSP and the third-party audit end; the private key is the multiplication cyclic group Z q * The first random number in; the public key belongs to the multiplicative cyclic group Z p * ; S3, user data preprocessing step: the user first generates a file identifier for the file to be processed; divides the file into multiple data blocks, and selects a secret random value for the file, the secret random value being a multiplication cyclic group Z q * The second random number in the data block; the value information of each data block is recorded at the same time to obtain a data block value information set; for each data block, the user needs to select a random chameleon hash auxiliary value for it; according to the user's private key, the securely encrypted chameleon hash function and the chameleon hash auxiliary value, the user generates a corresponding data block label for each data block and generates a data block label set; then, the user and the CSP agree on the generation rules of the data index table; finally, the user sends the file data block, the data block signature set, the chameleon hash auxiliary value set, the data block value information set and the generation rules to the CSP; S4, cloud data preprocessing step: after receiving the file data block, data block signature set, chameleon hash auxiliary value set, data block value information set and generation rules sent by the user, the CSP performs correctness verification processing on the data block and its corresponding data block label according to the user's public key. When the verification is correct, the user is informed that the user can delete the local file data block and data block label set; then, the CSP uses the data block value information set and generates a data index table according to the generation rules agreed with the user; in addition, according to the chameleon hash auxiliary value set, new chameleon hash auxiliary values ​​are calculated for all the expanded block indexes in the data index table to obtain a new chameleon hash auxiliary value set; S5, challenge step: when the user needs to verify the data integrity, the user sends an audit request to the third-party audit terminal. After receiving the request, the third-party audit terminal generates a challenge index set and a random number set, obtains a challenge set based on the challenge index set and the random number set, and sends the challenge set to the CSP; S6, evidence generation step: After receiving the challenge set sent by the third-party audit end, the CSP calculates the challenged data block, the data proof, label proof, auxiliary proof parameters, new chameleon hash auxiliary value set and original data block proof parameters of the challenge data block according to the challenge index set and the data index table in the challenge set; then, the CSP generates a proof set with the data proof, label proof, auxiliary proof parameters, new chameleon hash auxiliary value set and original data block proof parameters, and sends the proof set to the third-party audit end; S7, evidence verification step: After receiving the proof set, the third-party audit end performs data integrity verification processing, generates an integrity verification result, and sends the integrity verification result to the user.

2. The method for public integrity audit of multi-value density cloud storage data according to claim 1 is characterized in that: The parameter preprocessing step specifically includes: S11, obtain the multiplication cyclic group Z of two pre-selected large prime numbers p and q, p p * The multiplicative cyclic group Z of q q * and the multiplicative cyclic group Z p * The generator g of ; where p and q satisfy the relationship q|(p-1), and the order of g is a prime number q; S12, define a cryptographically secure chameleon hash function CH:{0,1}*→Z q * , where the role of the secure cryptographic chameleon hash function CH is to map a string of any length {0,1}* to a multiplicative cyclic group Z q * In the elements, let sk ch The private key of the Chameleon hash function CH shared by the user and CSP; S13, public system parameters {p,q,Z p * ,Z q * ,g,CH}.

3. The method for public integrity audit of multi-value density cloud storage data according to claim 2 is characterized in that: The user key generation step specifically includes: S21, the user generates a key pair, namely the private key sk U =s, public key pk U =y; where s∈Z q * A random number selected by the user, y = g s ∈Z p * ; S22, the user saves the private key sk U , and the public key pk U Published to CSP and third-party auditors.

4. The method for public integrity audit of multi-value density cloud storage data according to claim 3 is characterized in that: The user data preprocessing step specifically includes: S31, the user first generates a file identifier name for the file F to be processed, and divides the file F into n data blocks to obtain file data block F = {m i |1≤i≤n}; where m i For the i-th data block, randomly select α∈Z q * As a secret random value, calculate u = g α ∈Z p * , and make u public; S32, record the value information of each data block to obtain the data block value information set V = {V i |1≤i≤n}; where V i is the data block m i The value level of S33, for each data block m i , the user selects a random chameleon hash auxiliary value k for it i , get the Chameleon Hash auxiliary value set K = {k i |1≤i≤n}, and calculate the corresponding r based on it i =g ki ; S34, the user provides each data block m i Generate the corresponding data block label σ i , get the data block label set Φ, the calculation formula is: σ i =sk U ·CH(name||i,k i )+α·m i -k i ·r i ; Among them, σ i is the data block label of the i-th data block, name is the file identifier, and the data block label set Φ = {σ i |1≤i≤n}; S35, then, the user and CSP agree on the generation rule R of the data index table DIT, that is, how to extend the index for each data block according to its value level; assuming that data block m i The value level is V i , CSP adds V to this data block i-1 indexes, there are a total of V i An index points to the data block; after the indexes of all data blocks are expanded, the final data index table DIT is obtained; S36, finally, the user sends the file data block F, the data block signature set Φ, the chameleon hash auxiliary value set K, the data block value information set V and the generation rule R to the CSP.

5. The method for public integrity audit of multi-value density cloud storage data according to claim 4 is characterized in that: The cloud data preprocessing step specifically includes: S41, after receiving the file data block F, data block signature set Φ, chameleon hash auxiliary value set K, data block value information set V and generation rule R sent by the user, the CSP verifies the correctness of the data block and its corresponding data block label according to the user's public key. When the verification is correct, the user is informed, and the user can delete the local data block and the data block label set; The verification formula for the correctness verification process is: Among them, r i According to the chameleon hash auxiliary value k i The calculation formula is: S42, then, the CSP needs to generate a data index table DIT using the data block value information set V and a data index table DIT generation rule R agreed with the user; The mapping relationship between the expanded block index and the original data block index in the data index table DIT is recorded as S43, in addition, using the chameleon hash auxiliary value set K for all the extended block indexes in the data index table DIT, new chameleon hash auxiliary values ​​k are calculated respectively. j ', get a new chameleon hash auxiliary value set K' = {k j '|1≤j≤m}, where m is the number of indexes in the data index table; Among them, the original chameleon hash value corresponding to the current block and the newly calculated chameleon hash value satisfy the following relationship: CH(name||i,k i )=CH(name||j,k′ j ); Therefore, the new Chameleon Hash auxiliary value calculation formula is as follows: Among them, sk ch The private key of the Chameleon hash function that the user shares with the CSP.

6. The method for public integrity audit of multi-value density cloud storage data according to claim 5 is characterized in that: The challenge steps specifically include: S51, when the user needs to verify the data integrity, an audit request is sent to the third-party audit terminal; S52, after receiving the request, the third-party audit terminal generates a challenge index set Ψ and a random number set S. First, a challenge index set Ψ containing c elements is generated. l |1≤l≤c,c≤n}; where c is the number of challenge blocks, ψ l is the index number of the lth challenge block; S53, then generate a corresponding random number s for each challenge index l , get the random number set S = {s l |1≤l≤c}; where s l Z q * The third random number in ; S54, the third-party audit end obtains a challenge set Chal={Ψ,S} according to the challenge index set Ψ and the random number set S, and sends the challenge set Chal to the CSP.

7. The method for public integrity audit of multi-value density cloud storage data according to claim 6 is characterized in that: The evidence generation step specifically includes: S61, after the CSP receives the challenge set Chal sent by the third-party audit terminal, the challenge index set Ψ and the random number set S in the challenge set Chal are calculated according to the data index table DIT to generate the data proof μ of the challenge data block, and the calculation formula is: in, Indicates that the original data index is data block; x is a random mask used to protect data privacy; S62, according to the data index table DIT, the challenge index set Ψ and the random number set S in the challenge set Chal are calculated to generate the corresponding label proof σ, and the calculation formula is: in, The original data index is The label corresponding to the data block; S63, calculating the auxiliary proof parameter Q according to the challenge index set Ψ and the random number set S in the challenge set Chal, the calculation formula is: S64, according to the data index table DIT and the challenge set Chal, the original data block proof parameter r is calculated, and the calculation formula is: in, According to the Chameleon Hash auxiliary value The calculation formula is: S65, generate all proof sets Proof = {μ, σ, Q, K', r}, and send the proof set to a third-party audit terminal; wherein K' is a new chameleon hash auxiliary value set generated by the CSP.

8. The method for public integrity audit of multi-value density cloud storage data according to claim 7 is characterized in that: The evidence verification step specifically includes: S71, after the third-party audit end receives the proof set Proof, it performs data integrity verification processing, and the integrity verification formula is: S72, when it is determined that the integrity verification formula is established, a CSP correctly stores data successfully signal is generated, and the CSP correctly stores data successfully signal is sent to the user; S73: When it is determined that the integrity verification formula is not established, a CSP correctly storing data failure signal is generated, and the CSP correctly storing data failure signal is sent to the user.

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