Autonomous and controllable data sharing method in complex scene

Abstract

The invention discloses an autonomous and controllable data sharing method in a complex scene, which is used for supporting the security, privacy and multi-scene availability of personal data when the personal data is shared externally. And meanwhile, an access strategy and a complex scene access task are introduced, so that the authorization and access controllability and compliance of the personal data are further ensured. According to the method, multiple cryptographic algorithms including a Shamir secret sharing method, a rapid attribute-based encryption technology and the like are adopted to protect the confidentiality of public data. Wherein the rapid attribute-based encryption technology encrypts personal data and packages the personal data into a data capsule under a specified access strategy. In addition, the personal data owner can also generate access tasks according to different data use scenes (sequential, cooperative and shared scenes), and the sharing of the data in different scenes is limited. And finally, the personal data owner can cancel the access authority of the data user to the capsule through the cancel 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

[0003] The traditional "one-to-one" data sharing model can no longer meet the needs of complex business scenarios. In these scenarios, there is a need for multiple people to process the same data simultaneously, in parallel, or in sequence. For example, when a will is published, the testator's multiple children must be present at the same time to make the contents of the will public; in legal services, the client needs to authorize all members of the legal team to review the materials; in the passport application process, multiple staff members need to review and process the documents in sequence.

[0004] Therefore, the present invention uses 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 of multi-person collaboration, sequential and parallel processing; on this basis, it realizes selective sharing of data, informed consent authorization and permission revocation. Summary of the invention

[0005] 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.

[0006] An autonomous and controllable data sharing method in complex scenarios, the 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, data capsule decryption. The present invention mainly includes four types of roles: trusted institutions, personal data owners, data users and cloud storage.

[0007] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0008] 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 for the initialization of the system encryption scheme;

[0009] Step 2. Data user registration: The data user submits an application to the trusted institution, and the trusted institution generates a decryption key for the data user based on the data user's ID and attribute set;

[0010] Step 3. Data capsule encapsulation: The personal data owner packs his 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;

[0011] Step 4. Access task generation: The personal data owner generates different access tasks according to sequential, collaborative or shared data usage scenarios, and outputs access tasks and download tokens;

[0012] 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;

[0013] Step 6. Access task decryption: After receiving the access task, the data user decrypts the download token and semi-decryption result from the access task;

[0014] 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;

[0015] Step 8. Data capsule update: Cloud storage updates the data capsule using the revocation token;

[0016] 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.

[0017] The beneficial effects of the present invention are as follows:

[0018] A self-controllable data sharing method in complex scenarios is proposed, including encapsulation of personal data into capsules, authorization access strategy for personal data, and authorization of access tasks in complex scenarios. The main advantages of the present invention are as follows:

[0019] (1) Data security. An autonomous and controllable data sharing method in complex scenarios adopts 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 using access policies, access conditions and other restrictions, the access scenarios of data capsules that encapsulate personal data are restricted, reducing the possibility of personal data in data capsules being accessed by unauthorized users.

[0020] (2) Applicable to complex scenarios. The access task token of 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 choose different access task types according to different scenarios of personal data use, output three different access tasks, and realize selective sharing of multiple different data users and different data at the same time.

[0021] (3) Data autonomy and controllability. Based on data capsules with fine-grained authorization mechanisms and access task token mechanisms, a data sharing method that is autonomous and controllable in complex scenarios is proposed to achieve autonomous sharing of personal data. Specifically, access task tokens are used to achieve selective data sharing, informed consent authorization, and partial updates to data capsules to revoke data users’ access rights.

[0022] Key features include:

[0023] (1) Data user registration. After joining, the data user must first apply for a decryption key from a trusted institution. When applying, the applicant must submit his / her ID. 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, the decryption key can be used to decrypt the data in the capsule.

[0024] (2) Data capsule encapsulation and access task generation. The data owner can encapsulate any number of personal data through the data capsule encapsulation algorithm proposed in the present invention to form an integrated 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 according to 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

[0025] Figure 1 System architecture diagram of an embodiment of the present invention; 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 easy to understand, the present invention is further described below in conjunction with 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 frame:

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

[0031] (2) Data users: After joining the system, data users will be automatically assigned an identifier ID and can request a trusted institution to generate a decryption key based on their attribute set and ID. With the decryption key, data users can receive and decrypt access tasks, obtain download tokens, send them to the cloud storage, and obtain data capsules. According to the needs of the access scenario, data users use the decryption key and access tasks to restore the content in the data capsule.

[0032] (3) Personal data owner: The personal data owner can encapsulate his 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. In 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: It provides a large amount of storage space for the system, 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 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 in turn. At the request of the personal data owner, cloud storage can update the data capsule according to the revocation token of the personal data owner 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 mechanism 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 three hash functions, H1:{0,1} * →G1, It means 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 length of the data particle.

[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 for user u u :{S,sk1,{sk 2,s} s∈S ,sk3,sk4}.

[0043] Step 3:

[0044] The personal data owner runs DCEncap(mp k ,DG n ,(M,π)) will be the 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 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 the verification parameters Output the data capsule identifier DCI, the 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 can do so by running 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, according to 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 chained 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, he 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 the 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 need for collaborative access, that is, among the m data users, (satisfy ) user is present to decrypt the access task and proceed to the next step. 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 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 ), and 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 the 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 true, 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 representing the data user u;

[0071] All data users involved in decryption calculate Perform aggregation Then by checking the equation Whether the decryption is completed correctly is determined by whether the equation is true. If the equation is not true, the calculation is wrong. Otherwise, the calculation 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: The specific method is as follows:

[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 established access policy (M, π), then 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 granule 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] Comparative test of 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 in that: 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 for the initialization of the system encryption scheme; Step 2. Data user registration: The data user submits an application to the trusted institution, and the trusted institution 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 packs his 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 personal data owner generates different access tasks according to sequential, collaborative or shared data usage scenarios, and outputs 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-decryption 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 updates the data capsule using the revocation token; 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.

2. According to the method of claim 1, the autonomous and controllable data sharing method in complex scenarios is characterized in that: Step 1: When the trusted mechanism 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 from the G1 group g1 , a generator in a G2 group g2 ; Next, select three hash functions, H1: {0, 1} * →G1, H2: G T →{0, 1} l , It means 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}.

3. According to the method of claim 2, the autonomous and controllable data sharing method in complex scenarios is characterized in that: 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 Output data decryption key sk for user u u : {S, sk1, {sk 2，s } s∈S , sk3, sk4}.

4. According to claim 3, the autonomous and controllable data sharing method in complex scenarios is characterized in that: Step 3: The personal data owner runs DCEncap(mpk,DG n , (M, π)) will be the 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 equation; for k∈[c], select a random parameter a k ∈{0, 1} l , calculate Then randomly select parameters And calculate in DG n XOR all elements in; M is a matrix with n1 rows and n2 columns, which is the access strategy; two vectors are randomly selected 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; Calculate intermediate parameters And the verification parameters Output the data capsule identifier DCI, the 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.

5. According to claim 4, the autonomous and controllable data sharing method in complex scenarios is characterized in that: 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), I u ∈I m It represents the set of data granule indexes 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 T u，w，2 =P u，T ·P u，w , 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 chained 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, he must rely on the result obtained by the uth data user processing business for the data owner. In short, in the scenario of sequential access tasks, when the u+1th data user accesses the data capsule, the uth data user has completed the 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 xx = 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 need for collaborative access, that is, among the m data users, 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 q2(x) mod p satisfies q2(0) = k1; then, calculate the intermediate parameters 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 the data capsule, the access task can be decrypted independently; 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 The access task and download token are sent to the corresponding data user and cloud storage respectively.

6. According to claim 5, the autonomous and controllable data sharing method in complex scenarios is characterized in that: Step 5: 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: First, randomly select parameter a c+1 ∈{0, 1} l , calculate make Then calculate in Output R = {R1, R2 = DCI', R3 = a c+1 }.

7. The autonomous and controllable data sharing method in complex scenarios according to claim 6 is characterized in that: 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 the 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 ), and then calculate the proof of work of this node Final calculation Send the 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) is established 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 the 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 ID u The identifier ID representing the data user u; All data users involved in decryption calculate Perform aggregation Then by checking the equation Whether the decryption is completed correctly is determined by whether the equation is established; if the equation is not established, 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 .

8. The autonomous and controllable data sharing method in complex scenarios according to claim 7 is characterized in that: 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 if consistent.

9. The autonomous and controllable data sharing method in complex scenarios according to claim 8, characterized in that: 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 updating DCI′=R2, V′=R1;output and DCI′.

10. The autonomous and controllable data sharing method in complex scenarios according to claim 9, characterized in that: Step 9: The specific method is as follows: 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 FABE0 attribute decryption scheme, if the attributes of user u satisfy the established access policy (M, π), then a set of coefficients {γ i } i∈I So that∑ i∈I γ i M i =(1, 0, 0, ..., 0), where the set 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 granule 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

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