An autonomous and controllable data sharing method in complex scenarios

Abstract

The present invention discloses an autonomous and controllable data sharing method in complex scenarios, which is used to support the security, privacy and multi-scenario availability of personal data when personal data is shared externally. At the same time, access policies and complex scenario access tasks are introduced to further ensure the controllability and compliance of authorization and access to personal data. The present invention adopts a variety of cryptographic algorithms, including Shamir's secret sharing method, fast attribute-based encryption technology, etc., to protect the confidentiality of public data. Among them, the fast attribute-based encryption technology encrypts personal data under a specified access policy and packages it into a data capsule. In addition, the owner of personal data can also generate access tasks according to different data usage scenarios (sequential, collaborative and shared scenarios) to limit the sharing of data in different scenarios. Finally, the owner of personal data can revoke the data user's access rights to the capsule through a revocation request.

Description

Technical Field

[0001] The present invention belongs to the field of cryptographic algorithms and application systems, and specifically relates to an autonomous and controllable data sharing method in complex scenarios. Background Art

[0002] The traditional "one-to-one" data sharing model no longer meets the needs of complex business scenarios. In these scenarios, multiple people are required to process the same data simultaneously, in parallel, or sequentially. For example, when publishing a will, the testator's children must be present for the contents to be made public; in legal services, clients must authorize all members of the legal team to review the materials; and in the passport application process, multiple staff members must review and process documents sequentially.

[0003] Therefore, this invention utilizes data capsules with a fine-grained authorization mechanism, combined with autonomous and controllable access task tokens, to achieve autonomous and controllable data sharing in complex environments. Through a novel and collusion-resistant access task token construction algorithm, it supports complex scenarios involving multi-person collaboration, sequential and parallel processing, and thus enables selective data sharing, informed consent authorization, and permission revocation. Summary of the Invention

[0004] The purpose of this invention is to design an autonomous and controllable data sharing method in complex scenarios to achieve safe and efficient sharing of personal data in complex scenarios.

[0005] A self-controllable data sharing method for complex scenarios. Its main functional modules include: system initialization, data user registration, data capsule encapsulation, access task generation, revocation token generation, access task decryption, data capsule download, data capsule update, and data capsule decryption. This invention primarily involves four roles: trusted institutions, personal data owners, data users, and cloud storage.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] Step 1. System initialization: When establishing an autonomous and controllable data sharing method in complex scenarios, initialization parameters are constructed according to various standards, mainly focusing on the initialization of the system encryption scheme;

[0008] Step 2. Data user registration: The data user submits an application to the trusted institution, which generates a decryption key for the data user based on the data user's ID and attribute set;

[0009] Step 3. Data capsule encapsulation: The personal data owner packages his or her personal data into a data capsule, uses the constructed access control structure to encapsulate the data capsule, obtains the data capsule, and uploads the capsule to cloud storage;

[0010] Step 4. Access task generation: The individual data owner generates different access tasks based on sequential, collaborative, or shared data usage scenarios, and outputs the access tasks and download tokens.

[0011] Step 5. Revocation token generation: The personal data owner generates a revocation token that can be used by the cloud storage to update the data capsule;

[0012] Step 6. Access task decryption: After receiving the access task, the data user decrypts the download token and semi-decrypted result from the access task;

[0013] Step 7. Data capsule download: The data user uses the download token to send a data capsule download request to the cloud storage to obtain the data capsule;

[0014] Step 8. Data capsule update: Cloud storage uses the revocation token to update the data capsule;

[0015] Step 9. Data capsule decryption: The data user uses the access task and the semi-decryption result to decrypt the data capsule and output the data granule.

[0016] The beneficial effects of the present invention are as follows:

[0017] This paper proposes an autonomous and controllable data sharing method for complex scenarios, including the encapsulation of personal data into capsules, the authorization of personal data access policies, and the authorization of access tasks in complex scenarios. The main advantages of this invention are as follows:

[0018] (1) Data security. A self-controlled data sharing method in complex scenarios uses a variety of advanced, secure, and efficient cryptographic schemes, such as Shamir threshold secret sharing technology and attribute-based encryption technology, to achieve in-depth protection of the security of personal data. Secondly, by utilizing access policies, access conditions, and other restrictions, the access scenarios of the data capsules that encapsulate personal data are restricted, reducing the possibility of unauthorized users accessing the personal data in the data capsules.

[0019] (2) Applicable to complex scenarios. An access task token for an autonomous and controllable data sharing method in complex scenarios supports three complex scenarios: multi-person collaborative processing, multi-person sequential processing, and multi-person parallel processing. Personal data owners can select different access task types based on different scenarios of personal data use, output three different access tasks, and achieve selective sharing of multiple different data users and different data at the same time.

[0020] (3) Data autonomy and controllability. Based on a data capsule with a fine-grained authorization mechanism and an access task token mechanism, a data sharing method with autonomy and controllability in complex scenarios is proposed, which realizes the autonomy of personal data sharing. Specifically, the access task token is used to realize data selective sharing, informed consent authorization, and partial update of data capsules to revoke the access rights of data users.

[0021] Key features include:

[0022] (1) Data user registration. After joining, the data user must first apply for a decryption key from a trusted institution and submit the applicant's ID when applying. u And the applicant's attribute set S. The trusted institution generates a decryption key sk for it u ,When the data user obtains the data capsule and accesses the task, he can use the decryption key to decrypt the data in the capsule.

[0023] (2) Data capsule encapsulation and access task generation. The data owner can encapsulate any number of personal data using the data capsule encapsulation algorithm proposed in this invention to form a complete data capsule. In addition, during the data capsule encapsulation process, an access policy is embedded to protect the data capsule, so that only data users whose access attributes meet the specified access policy can access the data capsule. By adding an integrity verification value to the data capsule, the data capsule is protected from tampering. Finally, the data owner can also specify certain data in the data capsule that need to be shared and generate corresponding access tasks based on different usage scenarios. Data users can only obtain and decrypt the data capsule if they meet the access policy and have the corresponding access task at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 System architecture diagram of an embodiment of the present invention;

[0025] Figure 2 、 Figure 3 This paper presents an experimental analysis of an autonomous and controllable data sharing method in complex scenarios. DETAILED DESCRIPTION

[0026] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below with reference to the accompanying drawings.

[0027]

[0028]

[0029] The system architecture design of the present invention is as follows: Figure 1 As shown, the present invention focuses on the part within the dotted box:

[0030] (1) Trusted institution: The trusted institution is responsible for the initialization of the system, including initializing the full attribute set U and setting the public parameters of the system, authorizing the data user based on the attribute set of the data user and generating the decryption key.

[0031] (2) Data users: After joining the system, data users are automatically assigned an identifier (ID) and can request a decryption key from a trusted institution based on their attribute set and ID. With the decryption key, data users can receive and decrypt access tasks, obtain a download token, send it to the cloud storage, and retrieve the data capsule. Based on the needs of the access scenario, data users use the decryption key and access task to restore the contents of the data capsule.

[0032] (3) Personal data owner: The personal data owner can encapsulate his or her personal data into a data capsule and upload the data capsule to cloud storage for hosting. Afterwards, the personal data owner can authorize the data capsule by issuing collaborative access tasks, shared access tasks, or sequential access tasks to several data users according to the actual scenario. At the fourth step, the personal data owner also sends a download token to the cloud storage to control the download request of the data capsule and revoke the access rights of the data user.

[0033] (4) Cloud storage: This system provides a large amount of storage space, mainly used to store the data capsules, download tokens, and authorization tokens of the personal data owner. At the same time, cloud storage also has a certain amount of computing power, which can verify the validity of the download token provided by the data user and decide whether to allow the data user to access the data capsule. At the request of the personal data owner, cloud storage can update the data capsule based on the personal data owner's revocation token to revoke the data user's access rights.

[0034] An autonomous and controllable data sharing method in complex scenarios includes the following steps:

[0035] Step 1:

[0036] When the trusted organization is initialized, first, a bilinear group (G1, G2, G T ,e), where G1, G2 are two p-order cyclic additive groups on the elliptic curve, G T is a cyclic multiplicative group of order p, and e is a Weil pairing: G1×G2→G T ,Right now Q∈G2 and a,b∈Z p , there is e(P a ,Q b )=e(P,Q) ab; Then randomly select a generator g1 from the G1 group and a generator g2 from the G2 group. Next, select four hash functions, H1:{0,1} * →G1, Indicates that there are two elements on the G2 group; τ represents the number of elements, n1 represents an integer, and the parameter l represents the data granule length.

[0037] The trusted authority randomly selects two random numbers And use it as the master private key msk, save msk locally, calculate And initialize all the security attribute sets U that may be needed; finally, output and publish the system parameters mpk:{p,G1,G2,G T ,e,g1,g2,h1,h2,h,H1,H2,H3,U}.

[0038] Step 2:

[0039] When a data user registers, he first sends his attribute set S to the trusted institution, and the trusted institution generates a decryption key sk for him after receiving it. u The specific process is as follows:

[0040] First, select a random number calculate And for each attribute s in the attribute set S, calculate sk 2,s =H1(s) r ; Then use α,β in msk to calculate

[0041] sk3=H1(ID u ) α H1(|U|+1) r ,sk4=H1(ID u ) β

[0042] Output data decryption key sk of user u u :{S,sk1,{sk 2,s} s∈S ,sk3,sk4}.

[0043] Step 3:

[0044] The owner of personal data runs DCEncap(mp k ,DG n ,(M,π)) personal data DG n Encapsulated into a data capsule, (M,π) represents the access strategy, and the algorithm flow is as follows:

[0045] First, initialize the parameter c=1 and the data particle set DG n The length of the data particle l in the equation; for k∈[c], select a random parameter a k ∈{0,1} l , calculate Then randomly select parameters and calculate in Refers to DG n XOR all elements in; M is a matrix with n1 rows and n2 columns, which is the access strategy; randomly select two vectors For j∈[τ], calculate the intermediate parameters For i∈[n1], calculate M i Represents the i-th row of the matrix M; ρ(i) represents the mapping of the i-th row attribute; calculates the intermediate parameters And verification parameters Output the data capsule identifier DCI, data capsule DC and the secret parameter L stored locally by the personal data owner; After the capsule is generated, it is uploaded to the cloud storage.

[0046] Step 4:

[0047] The owner of personal data runs The algorithm is a set of m data users DU m Generate an authorized data capsule access task token. Depending on the access scenario, three types of access tasks can be generated:

[0048] First decode the secret parameters For data user u∈DU m , first calculate the intermediate parameter P u,T =e(H1(ID u ) y ,h1), I u ∈I m Represents the set of data particle subscripts that the data owner wants to share with data user u ( Where n is the number of shared data particles), for the intermediate parameter w∈I u , randomly select parameters Calculate intermediate parameters Let the intermediate parameter T u,2 ={T u,w,1,T u,w,2 Then, based on the authorization scenario, select one of the access tasks to generate.

[0049] (1) Sequential access tasks

[0050] Assume that the group of data users u∈DU m When accessing data capsules, there is a need for chain access, that is, when the data user u∈DU m In the access chain, when the u+1th data user processes business for the data owner, it must rely on the results obtained by the uth data user processing business for the data owner. Simply put, in the scenario of sequential access tasks, when the u+1th data user accesses the data capsule, the uth data user has already completed access to the data. In this scenario, the algorithm flow is as follows:

[0051] First calculate the intermediate parameters Then randomly initialize an m-1 degree polynomial ψ(x), and set the intermediate parameter q1(x) = ψ(x) + y mod p. Set the intermediate parameter For u∈DU m , calculate the intermediate parameter x u =H2(e(H1(ID u ) d ,h2·X·Y u-1 )), Z u =X·Y u-1 ,Y u-1 Represents the result obtained by data user u-1 after completing the access to the data; let

[0052] (2) Collaborative access tasks

[0053] Assume that the group of data users DU m When accessing data capsules, there is a collaborative access requirement, that is, among the m data users, there are (satisfy ) user is present to decrypt the access task and proceed to the next steps. In this scenario, the process is as follows:

[0054] First, randomly select the parameters And initialize a The polynomial q2(x) mod p satisfies q2(0) = k1. Then, calculate the intermediate parameters For u∈DU m , calculate the intermediate parameters make

[0055] (3) Shared access tasks

[0056] Assume that the group of data users DU m When accessing the data capsule, the access task can be decrypted independently.

[0057] In this scenario, the algorithm flow is as follows:

[0058] First randomly select For u∈DU m , calculate x u =H2(e(H1(ID u ) d ,h2)). Then initialize the polynomial Recalculate make

[0059] Output access tasks Download Token The access task and download token are sent to the corresponding data user and cloud storage respectively.

[0060] Step 5:

[0061] When the data owner wants to revoke the user's access to the capsule, he or she generates a revocation token R and sends it to the cloud storage, entrusting the cloud storage to update the capsule. The specific process is as follows:

[0062] First, randomly select the parameter a c+1 ∈{0,1} l , calculate make Then calculate in Output R = {R1, R2 = DCI', r3 = a c+1}.

[0063] Step 6:

[0064] Data user u decrypts the access token to obtain the download token DT u and the semi-decrypted result C y The access token decryption process uses different methods depending on the scenario:

[0065] (1) Sequential access task decryption

[0066] 1. If the data user u performing decryption is the head node of the sequential access chain, first calculate x u =H2(e(sk4,DCI))=H2(e(H1(ID u),g2) dβ ), then calculate the proof of work of this node Final calculation Send the proof of work π1,π2 to the next node.

[0067] 2. If the data user u performing decryption is not the head node of the sequential access chain, first check the following equation e(π1,Z u )=e(AC,g2) to determine whether the work of the previous node of the current node is completed correctly. If the equation is not established, it means that the previous node has not completed the work correctly, and the algorithm process is terminated. Otherwise, calculate Then calculate the proof of work of the current node Final calculation Send the proof of work π1,π2 to the next node.

[0068] (2) Collaborative access task decryption

[0069] Assume that the set of users participating in decryption is DU m' ,satisfy The decryption process is as follows:

[0070] For the decrypted data user u∈DU m' , first restore x locally u =H2(e(sk4,DCI)), calculate in calculate ID u The identifier ID of the data user u;

[0071] Calculated by all data users participating in decryption Perform aggregation Then by checking the equation Is it true to judge whether the decryption is completed correctly. If the equality is not true, the calculation is wrong. Otherwise, calculate A C,1 and A C,2 Respectively represent A C The first and second elements in ;

[0072] (3) Shared access task decryption

[0073] The decrypted data user u first calculates x u =H2(e(sk4,DCI)). Then, restore k2=q3(x u ) and calculate

[0074] The decryption finally outputs the download token DT u And the semi-decrypted result C y .

[0075] Step 7:

[0076] The data user sends a download request to the cloud storage, and the cloud storage first checks the time t when the download request is received. now Is it in D u,2 If it is within the acceptable range, then check DT u Whether and D u,1 Consistent, output the complete data capsule DC if consistent.

[0077] Step 8:

[0078] The data owner sends a revocation request R to the cloud storage, and the cloud storage uses the revocation request R to update the corresponding data capsule. The specific process is as follows:

[0079] First update DCI'=R2, V'=R1. Output and DCI'.

[0080] Step 9:

[0081] After receiving the data capsule and access task, the data user restores the complete data particle through three stages of calculation. The specific process is as follows:

[0082] (1) Data capsule integrity verification phase: First calculate Re-judge Whether the equation is true or not is used to determine whether the DC is intact or tampered with. If the equation is not true, the process is terminated.

[0083] (2) Attribute verification phase: Using the attribute decryption scheme of FABEO, if the attributes of user u satisfy the access policy (M,π), a set of coefficients {γ i} i∈I So that ∑ i∈I γ i M i =(1,0,0,…,0), where set I refers to the intersection of the user attribute set and the set used in the access policy. The algorithm can reconstruct P in the following way u,T :

[0084]

[0085] (3) Data granularity recovery phase: First, use the reconstructed P u,T calculate Afterwards, for (T u,w,1 ,T u,w,2 )∈T u,2 , using the reconstructed P u,T calculate Output

[0086] Conduct comparative tests on algorithm efficiency.

[0087] The experimental environment is Windows 10, Intel(R)Core(TM)i7-7700HQ CPU@2.80Ghz, 16gbRAM. The experimental test used Python3.6.9 and relied on cryptographic libraries such as PBC-0.5.14 and Charm-0.50. The curve used in the test is MNT-224. In terms of parameter setting, this article sets l=128. Since the data encrypted and shared by the DZS scheme and the NJY+ scheme are both one data, this article sets n=1. In addition, the DZS scheme and the NJY+ scheme do not support attribute reuse, so this article sets τ=1. In practical applications, |U|>>|S|, therefore, the present invention sets |U|=100, |S|=n1=10. According to the results of theoretical analysis, an experimental analysis of an autonomous and controllable data sharing method in complex scenarios is as follows. Figure 2 and Figure 3 shown.

Claims

1. An autonomous and controllable data sharing method in complex scenarios, characterized by: The steps include: Step 1. System initialization: When establishing an autonomous and controllable data sharing method in complex scenarios, initialization parameters are constructed according to various standards, mainly focusing on the initialization of the system encryption scheme; Step 2. Data user registration: The data user submits an application to the trusted institution, which generates a decryption key for the data user based on the data user's ID and attribute set; Step 3. Data capsule encapsulation: The personal data owner packages his or her personal data into a data capsule, uses the constructed access control structure to encapsulate the data capsule, obtains the data capsule, and uploads the capsule to cloud storage; Step 4. Access task generation: The individual data owner generates different access tasks based on sequential, collaborative, or shared data usage scenarios, and outputs the access tasks and download tokens. Step 5. Revocation token generation: The personal data owner generates a revocation token that can be used by the cloud storage to update the data capsule; Step 6. Access task decryption: After receiving the access task, the data user decrypts the download token and semi-decrypted result from the access task; Step 7. Data capsule download: The data user uses the download token to send a data capsule download request to the cloud storage to obtain the data capsule; Step 8. Data capsule update: Cloud storage uses the revocation token to update the data capsule; Step 9. Data capsule decryption: The data user uses the access task and the semi-decryption result to decrypt the data capsule and output the data granule; Step 1: When the trusted organization is initialized, first, a bilinear group (G1, G2, G T ,e), where G1, G2 are two p-order cyclic additive groups on the elliptic curve, G T is a cyclic multiplicative group of order p, and e is a Weil pairing: G1×G2→G T ,Right now Q∈G2 and a,b∈Z p , there is e(P a ,Q b )=e(P,Q) ab ; Then randomly select a generator g1 from the G1 group and a generator g2 from the G2 group; then select four hash functions, H1:{0,1} * →G1, Indicates that there are two elements on the G2 group; τ represents the number of elements, n1 represents an integer, and the parameter l represents the data particle length; The trusted authority randomly selects two random numbers And use it as the master private key msk, save msk locally, calculate And initialize all the security attribute sets U that may be needed; finally, output and publish the system parameters mpk:{p,G1,G2,G T ,e,g1,g2,h1,h2,h,H1,H2,H3,U}; Step 2: When a data user registers, he first sends his attribute set S to the trusted institution, and the trusted institution generates a decryption key sk for him after receiving it. u The specific process is as follows: First, select a random number calculate And for each attribute s in the attribute set S, calculate sk 2,s =H1(s) r ; Then use α,β in msk to calculate sk3=H1(ID u ) α ·H1(|U|+1) r ,sk4=H1(ID u ) β Among them, ID u Indicates the identifier ID of data user u and outputs the decryption key sk of data user u u :{S,sk1,{sk 2,s } s∈S ,sk3,sk4}; Step 3: The owner of personal data runs DCEncap(mpk,DG n ,(M,π)) personal data DG n Encapsulated into a data capsule, (M,π) represents the access strategy, and the algorithm flow is as follows: First, initialize the parameter c=1 and the data particle set DG n The length of the data particle l in the quanta; for k∈[c], select a random parameter calculate Then randomly select parameters and calculate in Refers to DG n XOR all elements in; M is a matrix with n1 rows and n2 columns, which is the access strategy; randomly select two vectors For j∈[τ], calculate the intermediate parameters For i∈[n1], calculate M i Represents the i-th row of the matrix M; ρ(i) represents the mapping of the i-th row attribute; calculates the intermediate parameters And verification parameters Output the data capsule identifier DCI, data capsule DC and the secret parameter L stored locally by the personal data owner; After the capsule is generated, it is uploaded to the cloud storage; Step 4: The owner of personal data runs The algorithm is a set of m data users DU m Generate an authorized data capsule access task token. Depending on the access scenario, three types of access tasks can be generated: First decode the secret parameters For data user u∈DU m , first calculate the intermediate parameter P u,T =e(H1(ID u ) y ,h1), Represents the set of data particle subscripts that the data owner wants to share with data user u, Where n is the number of shared data particles; for the intermediate parameter w∈I u , randomly select parameters Calculate intermediate parameters Let the intermediate parameter T u,2 ={T u,w,1 ,T u,w,2 }; Then, according to the authorization scenario, select one of the access tasks to generate; (1) Sequential access tasks Assume that the group of data users u∈DU m When accessing data capsules, there is a need for chain access, that is, when the data user u∈DU m In the access chain, when the u+1th data user processes business for the data owner, it must rely on the results obtained by the uth data user processing business for the data owner. Simply put, in the scenario of sequential access tasks, when the u+1th data user accesses the data capsule, the uth data user has already completed access to the data. In this scenario, the algorithm flow is as follows: First calculate the intermediate parameters Then randomly initialize an m-1 degree polynomial ψ(x), let the intermediate parameter q1(x)=ψ(x)+y mod p; let the intermediate parameter For u∈DU m , calculate the intermediate parameter x u =H2(e(H1(ID u ) d ,h2·X·Y u-1 )), Z u =X·Y u-1 ,Y u-1 Represents the result obtained by data user u-1 after completing the access to the data; let (2) Collaborative access tasks Assume that the group of data users DU m When accessing data capsules, there is a collaborative access requirement, that is, among the m data users, there are When a user is present, Only then can the access task be decrypted and the next steps be performed; in this scenario, the process is as follows: First, randomly select the parameters And initialize a The polynomial q29x) mod p is used to satisfy q2(0) = k1; then, the intermediate parameters are calculated. For u∈DU m , calculate the intermediate parameter x u =H2(e(H1(ID u ) d ,h2)), make (3) Shared access tasks Assume that the group of data users DU m When accessing a data capsule, each can independently decrypt the access task. In this scenario, the algorithm flow is as follows: First randomly select For u∈DU m , calculate x u =H2(e(H1(ID u ) d ,h2)); then initialize the polynomial Recalculate make Output access tasks Download Token Send access tasks and download tokens to corresponding data users and cloud storage respectively; Step 5: When the data owner wants to revoke the user's access to the capsule, he generates a revocation token R and sends it to the cloud storage, entrusting the cloud storage to update the capsule. The specific process is as follows: First, randomly select the parameters calculate make Then calculate in Output R = {R1, R2 = DCI', R3 = a c+1 }; Step 6: Data user u decrypts the access token to obtain the download token DT u and the semi-decrypted result C y The access token decryption process uses different methods depending on the scenario: (1) Sequential access task decryption If the data user u performing decryption is the head node of the sequential access chain, first calculate x u =H2(e(sk4,DCI))=H2(e(H1(ID u ),g2) dβ ), then calculate the proof of work of this node Final calculation Send proof of work π1, π2 to the next node; If the data user u performing decryption is not the head node of the sequential access chain, first check the following equation e(π1,Z u )=e(AC,g2) to determine whether the work of the previous node of the current node is completed correctly. If the equation is not established, it means that the previous node has not completed the work correctly, and the algorithm process is terminated; otherwise, the calculation Then calculate the proof of work of the current node Final calculation Send proof of work π1, π2 to the next node; (2) Collaborative access task decryption Assume that the set of users participating in decryption is DU m' ,satisfy The decryption process is as follows: For the decrypted data user u∈DU m' , first restore x locally u =H2(e(sk4,DCI)), calculate in calculate Calculated by all data users participating in decryption Perform aggregation Then by checking the equation Is it true to judge whether the decryption is completed correctly? If the equation does not hold, the calculation is wrong; otherwise, the calculation A C,1 and A C,2 Respectively represent A C The first and second elements in ; (3) Shared access task decryption The decrypted data user u first calculates x u =H2(e(sk4,DCI)); then, restore k2=q3(x u ) and calculate The decryption finally outputs the download token DT u And the semi-decrypted result C y ; Step 7: The data user sends a download request to the cloud storage, and the cloud storage first checks the time t when the download request is received. now Is it in D u,2 If it is within the acceptable range, then check DT u Whether and D u,1 Consistent, output the complete data capsule DC under consistent conditions; Step 8: The data owner sends a revocation request R to the cloud storage, and the cloud storage uses the revocation request R to update the corresponding data capsule. First, update Output and DCI'; Step 9: After receiving the data capsule and access task, the data user restores the complete data particle through three stages of calculation. The specific process is as follows: (1) Data capsule integrity verification phase: First calculate Re-judge Whether the equation is true or not is used to determine whether the DC is complete or tampered with; if the equation is not true, the process is terminated; (2) Attribute verification phase: Using the attribute decryption scheme of FABEO, if the attributes of user u satisfy the access policy (M,π), a set of coefficients {γ i } i∈I So that ∑ i∈I γ i M i =(1,0,0,…,0), where set I refers to the intersection of the user attribute set and the set used in the access policy; the algorithm can reconstruct P in the following way u,T : (3) Data granularity recovery phase: First, use the reconstructed P u,T calculate Afterwards, for (T u,w,1 ,T u,w,2 )∈T u,2 , using the reconstructed P u，T calculate Output

Images