Bidirectional reversible multi-layer location privacy protection method and system based on attribute encryption

Abstract

The invention relates to the technical field of privacy protection, in particular to a bidirectional reversible multi-layer position privacy protection method and system based on attribute encryption, and the method comprises the steps: constructing a road section undirected graph and an adjacency list; adding false nodes; constructing an anonymous data set of a plurality of security protection levels; the false information added at each anonymous level is marked hierarchically by utilizing a false information identification file, so that the false information is removed, and the reversibility of anonymous data and the bidirectionality of an anonymous process are ensured; establishing an access structure tree according to an access strategy of a false information identification file formulated by a data owner, and performing data encryption by taking an attribute set as a public key; an anonymous data set is generated and provided for a user to use, and the anonymous data set is published; encrypting the identification file, and generating an attribute key; accessing the identification file ciphertext according to the user attribute; and the privacy protection effect is measured. According to the method, the problem of coarse-grained privacy protection caused by'all 'or'neglect' rigid privacy protection is solved.

Description

Technical Field

[0001] The present invention relates to the field of privacy protection technology, and in particular to a bidirectional reversible multi-layer location privacy protection method and system based on attribute encryption. Background Art

[0002] With the development of mobile networks, applications and services based on mobile terminals have been widely used, and location-based services (LBS) have attracted great attention. However, while enjoying the convenience brought by LBS, users also leave a large amount of mobile location trajectory data on the service platform. Attackers can obtain users' privacy information by analyzing this data, including the user's location stored on the service platform, combined with the user's relevant background knowledge. For example, attackers can infer the user's health status by combining the user's location information near the hospital. In order to reduce the risk of privacy leakage in LBS, researchers have proposed many privacy protection methods, such as location obfuscation method, trajectory offset method, false information method, k-anonymity method, etc., which can better protect users' privacy information.

[0003] However, data protection is not the ultimate goal, but ensuring data efficiency while protecting privacy information from being leaked is an important goal. Current privacy protection methods basically adopt a rigid privacy protection standard of "all" or "none", either providing a unified standard of protection for privacy information or not protecting it. They cannot provide multi-granular privacy protection based on the actual needs of users, and cannot meet users' personalized and multi-level privacy protection needs. Moreover, the anonymization of data is one-way and irreversible. Once false data is added to the data set to achieve the goal of hiding the real location information, it can no longer be removed, and the de-anonymization reverse processing can no longer be performed. The data can no longer be restored to its original state, resulting in a permanent loss of location data quality and affecting data efficiency.

[0004] Therefore, based on the user's privacy information protection needs, solving the coarse-grained privacy protection problem caused by the rigid privacy protection of "all" or "none", providing multi-level and fine-grained privacy protection, and solving the problem of permanent loss of data quality caused by the irreversibility of anonymous data; realizing the reversible conversion of data anonymization and de-anonymization and effective control of resources is an urgent problem to be solved in location trajectory privacy protection. Summary of the invention

[0005] In view of the shortcomings of the existing methods, the present invention provides users with multi-level, reversible and fine-grained privacy protection, sets multiple privacy protection levels according to actual needs, adds differential false information layer by layer, and generates a series of false information identification files, so as to realize multi-level protection of location data privacy; by constructing a location adjacency table, randomly selecting location points using a hash function to increase security, encrypting the false information identification file using a method based on an access structure tree, controlling the degree of de-anonymization of users with different trust levels, obtaining user data of different precisions, and solving the problem of coarse-grained privacy protection caused by rigid privacy protection of "all" or "none"; formulating access policies based on an access control system of attribute encryption, taking the attributes of privileged users as encryption parameters, generating an access structure tree, and encrypting false information identification files; using a trusted third party to authenticate the attributes of the user, generating a decryption private key for the corresponding identification file ciphertext based on the user attribute value, so that the privileged user can perform the corresponding de-anonymization operation, realizing the reversible conversion and resource control of data anonymization and de-anonymization, reducing the complexity of key generation, distribution, use and management, and solving the problem of permanent data quality loss caused by the irreversibility of anonymous data.

[0006] The technical solution adopted by the present invention is: a bidirectional reversible multi-layer location privacy protection method based on attribute encryption includes the following steps:

[0007] Step 1: Based on the user location trajectory dataset, construct a road segment undirected graph and adjacency table; use hash functions and random numbers to add false nodes;

[0008] As a preferred embodiment of the present invention, adding a false node by using a hash function and a random number includes:

[0009] Use pseudo-random numbers and hash functions to determine the false nodes in the adjacency list;

[0010] Check the identification bit of a false node in the adjacency table. When the identification bit is 0, the false node is selected. When the identification bit is 1, the false node has a conflict, and a new false node is reselected using the open addressing method in the hash function.

[0011] Step 2: Based on the adjacency list, several levels of anonymous data sets are constructed according to the anonymity level and corresponding configuration parameters;

[0012] As a preferred embodiment of the present invention, step 2 specifically includes:

[0013] Step 21: Set the anonymity level to L i Divide into N levels according to the order of anonymity, and calculate the anonymity parameter corresponding to a certain anonymity level;

[0014] As a preferred implementation of the present invention, the formula of the anonymity parameter corresponding to a certain anonymity level is:

[0015] k i =k1+(i-1)k1=i·k1 (3)

[0016] Among them, 1≤i≤N, k1 is the anonymous parameter corresponding to L1.

[0017] Step 22, starting from the anonymity level L1, construct an anonymous data set according to the configuration parameters (k1, t1, d1) of L1, where t1 is the time threshold and d1 is the distance threshold; start adding false nodes; select the anonymous parameter k1 position points directly connected to the position in L0 in the current adjacency table to construct the anonymous data set M1 of L1;

[0018] As a preferred implementation of the present invention, when adding a fake node, a fake node is selected in the adjacency table by a random number until the configuration parameters of the anonymity level are met; if the user's anonymity condition is still not met after exceeding the distance threshold d1, the anonymity fails.

[0019] Step 23: Based on L1, add fake nodes to M1 anonymity until the configuration parameters (k2, t2, d2) of L2 anonymity level are met, and the constructed anonymous set is M2; continue to iterate and construct L N-1 Anonymous dataset M N-1 .

[0020] Step 3: Construct a de-anonymized false information identification file, and use the false information identification file to hierarchically mark the false information added at each anonymity level, so as to remove the corresponding false information during de-anonymization, and ensure the reversibility of anonymous data and the bidirectionality of the anonymization process;

[0021] As a preferred implementation of the present invention, the false information identification file corresponds to the set anonymity level, and the structure of the false information identification file is B=(ID, L, FID); wherein ID represents the serial number, L represents the degree of anonymity, and FID represents adding a false information identification.

[0022] Step 4: formulate an access policy for the false information identification file according to the data owner, establish an access structure tree, use the attribute set as the public key to encrypt the data, and the user who meets the attribute set obtains the decryption private key and decrypts the ciphertext data of the false information identification file according to the private key;

[0023] Step 5: The data owner generates a unified anonymous data set and provides it to users. All users face the same anonymous data set and publish it.

[0024] Step 6: Encrypt the identification file and generate an attribute key;

[0025] As a preferred implementation of the present invention, step six specifically includes:

[0026] Step 61: The data owner uses the public key PK to access the structure tree T i For identification file B i Encrypt and get the ciphertext CB i ;

[0027] Step 62: Set the master key MK and the access structure tree T i Send to TTP; send the anonymous data set and the ciphertext set of the identification file to DSP;

[0028] Step 63: User U i Attribute Certificate CA i Send to TTP to apply for the decryption private key SK of the corresponding identification file ciphertext;

[0029] Step 64: TTP accesses the structure tree T i , Attribute Certificate CA i , generate the decryption private key Sk of the corresponding identification file ciphertext i Send to user U i ; U i Using Sk i Decrypt the ciphertext of the identification file CB i , get false information identification file B i , and then remove false information from the anonymous dataset to obtain accurate data.

[0030] Step 7: Access the identification file according to user attributes;

[0031] As a preferred implementation of the present invention, ordinary users use unified anonymous data to obtain location data, and privileged users use de-anonymization keys to remove false information and obtain relatively accurate location data.

[0032] Step 8: Measure the effectiveness of privacy protection;

[0033] As a preferred implementation of the present invention, step eight specifically includes:

[0034] It is measured using location set entropy, location distance entropy, privacy protection efficiency, and data usage efficiency.

[0035] As a preferred embodiment of the present invention, a bidirectional reversible multi-layer location privacy protection system based on attribute encryption includes: a memory for storing instructions executable by a processor; a processor for executing instructions to implement a bidirectional reversible multi-layer location privacy protection method based on attribute encryption.

[0036] The present invention can be applied to scenarios where users with different trust levels access the same data resources, permissions are granted when the user identity is unknown, and data needs to be restored after being anonymized. The processing of location trajectory data is divided into seven parts: adding false information, generating false information identification files, formulating access control policies, publishing anonymous data sets, encrypting identification files, generating attribute keys, and user access to data. It combines privacy protection, data encryption, access control, and attribute encryption. The data owner sets the privacy protection level, generates corresponding identification files while adding false information layer by layer, and marks all false information added at this level. The data owner sets the access policy for each identification file, generates corresponding attribute keys, and Access the structure tree, and encrypt the identification file with the structure tree as a parameter to generate the identification file ciphertext; the data owner sends the final anonymous data set and several identification file ciphertexts to the data service center, and sends the access structure tree to the trusted third party; when the user attributes meet the conditions of the access structure tree, the decryption key of the corresponding identification file ciphertext can be obtained, the corresponding false information identification file can be decrypted, the false location information can be obtained and deanonymization operation can be performed, and a more accurate data set can be obtained at a certain privacy protection level; for ordinary users, since there is no key to decrypt the identification file ciphertext, only unified high-anonymity data can be used to better protect the user's privacy.

[0037] Beneficial effects of the present invention:

[0038] 1. Solve the problem of coarse-grained privacy protection caused by rigid privacy protection of "all" or "none". Set multiple privacy protection levels according to actual needs, add differential false information layer by layer, and generate a series of false information identification files to achieve multi-level protection of location data privacy;

[0039] 2. By constructing a location adjacency table and using a hash function to randomly select location points to increase security, the false information identification file is encrypted using a method based on an access structure tree, and the degree of de-anonymization of users with different trust levels is controlled, so that users with different permissions can ultimately obtain data with different anonymity levels and different precisions, thus achieving multi-level and fine-grained protection of user privacy information;

[0040] 3. Solve the problem of permanent data quality loss caused by the irreversibility of anonymous data. Formulate access policies based on the access control system of attribute encryption, use the attributes of privileged users as encryption parameters, generate access structure trees, and encrypt false information identification files; use a trusted third party to authenticate the user's attributes, and generate a decryption key for the corresponding identification file ciphertext based on the user's attribute values, so that privileged users can perform corresponding de-anonymization operations, realize reversible conversion and resource control of data anonymization and de-anonymization, reduce the complexity of key generation, distribution, use and management, and realize two-way reversible privacy protection;

[0041] 4. It has a wide range of uses and can be used to protect the location and trajectory privacy of mobile users, and is universally applicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the bidirectional reversible multi-layer location privacy protection method based on attribute encryption of the present invention;

[0043] Figure 2 (a), (b), and (c) are the road map, the road segment undirected graph, and the location adjacency list formed when constructing the anonymous set;

[0044] Figure 3 It is a diagram of the encryption release and de-anonymization process of the false information identification file of the present invention;

[0045] Figure 4 is a data availability diagram of the privacy protection of the present invention under different privacy protection parameters and levels;

[0046] Figure 5 It is a comparison chart of the privacy protection effect of the privacy protection method of the present invention and other related methods;

[0047] Figure 6 It is a comparison chart of the execution efficiency of the privacy protection method of the present invention and other related methods. DETAILED DESCRIPTION

[0048] The present invention is further described below in conjunction with the accompanying drawings and embodiments. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore it only shows the components related to the present invention.

[0049] like Figure 1 As shown in FIG, the bidirectional reversible multi-layer location privacy protection method based on attribute encryption includes the following steps:

[0050] Step 1: Based on the user location trajectory dataset, an undirected graph of road segments and an adjacency list are constructed. Before anonymization, all road segments in the map are preprocessed in a conflict-free manner to build an undirected graph of road segments. Based on the undirected graph, the corresponding adjacency list is generated. Figure 2 In the figure, (a) represents the city road map, (b) is the constructed undirected graph, and the final position adjacency table is shown in Figure (c).

[0051] When adding a fake node, first use a random function to generate a pseudo-random number, and then use a hash function to determine the selected fake node. The formula is:

[0052] H i (Key)=Key i mod|A| (1)

[0053] Among them, Key i is the pseudo-random number generated, |A| is the number of nodes in the adjacency list, mod is the remainder operation, H i (Key) is used to select a pseudo node in the adjacency list.

[0054] When selecting the connection point corresponding to the false node and the real position point in the adjacency list, first check the identification bit corresponding to the false node. If the identification bit is 0, the false node is selected; if the identification bit is 1, it means that the false node has been added to the anonymous set. If there is a conflict in the selected false node, the open addressing method in the hash function is used to resolve the conflict and reselect a new false node, that is:

[0055] H i (Key)=(H(Key)+d i )mod|A| (2)

[0056] Where i = 1, 2, ..., s, H(Key) is the hash function, d i is an incremental sequence, and the value selection method adopts the linear detection and rehashing method, d i =c×i, c=1.

[0057] Reselect the fake node by resolving the conflict. If there is still a conflict, continue to use the conflict hash function to select a new fake node until the position of the fake node without conflict is selected.

[0058] Step 2: Based on the adjacency list, construct a multi-level anonymous data set;

[0059] Step 21: According to the configuration parameters set by the user, the privacy protection requirements and the degree of anonymity, the anonymity level is divided into N levels, that is, the anonymity level of the data is divided into N levels, specifically L = {L0, L1, L2, L3, ... L i ,…L N-1}, and the anonymity level satisfies: L0 <L1<L2<L3<…<L i … <L N-1 ; Among them, L0 only contains real data information, L N-1 The highest degree of anonymity is the anonymity level of the anonymous data finally released; the anonymity level L i The corresponding anonymous parameters are:

[0060] k i =k1+(i-1)k1=i·k1 (3)

[0061] Among them, 1≤i≤N, k1 is the anonymous parameter corresponding to L1.

[0062] Step 22, anonymity processing starts from L1, and an anonymous data set is constructed according to the configuration parameters (k1, t1, d1) of the anonymity level, where t1 is the time threshold and d1 is the distance threshold; on the basis of the set L0 containing only real users, false nodes are added; first, k1 position points directly connected to the position in L0 are selected in the current adjacency table to construct an anonymous data set M1 at the L1 level; when adding false nodes, a greedy algorithm is used, and a random number within a certain range is generated using a random function and a hash function, and a false node is selected in the adjacency table through the random number until the configuration parameters of the anonymity level are met; if the set distance threshold d1 is exceeded and the user's anonymity condition is still not met, the anonymity fails;

[0063] Step 23, then, based on L1, continue to add false nodes to the anonymous set M1 until the configuration parameters (k2, t2, d2) of the L2 anonymity level are met. At this time, the constructed anonymous set is M2; wherein, Continue iterating until L is constructed N-1 Level anonymous dataset M N-1 So far, among them Finally, the anonymous dataset M is released N-1 .

[0064] Step 3. Create a de-anonymized false information identification file; the false information identification table is mainly used to hierarchically mark the false information added at each anonymity level, so that the corresponding false information can be accurately removed during the de-anonymization stage to obtain accurate data of different precisions, thereby ensuring the reversibility of anonymous data and the bidirectionality of the anonymization process; the false information identification file corresponds to the set anonymity level, and each anonymity level corresponds to an identification file.

[0065] The structure of the false information identification file is B = (ID, L, FID), where ID represents the serial number, L represents the anonymity level, and FID represents adding a false information identification; according to the set anonymity level L1 <L2<L3<…<L N-1 After adding the corresponding amount of false information in layers, the false information identification is layered to create an identification file, identification file B i and the anonymity level L i One-to-one correspondence, the lower the anonymity level, the more false location information marked in the identification file, the more false information can be removed by the de-anonymization operation, and the more accurate the data obtained.

[0066] The relationship between the identification files is: The number of false data in each level is |B i|; Each anonymity level only adds false information and generates false information identification files without publishing anonymous data. Finally, a unified anonymous dataset M is generated, and N-1 identification files with false information are generated.

[0067] Step 4: Formulate access strategy. The data owner formulates access strategy for the false information identification file, establishes access structure tree, and uses attribute set as public key to encrypt data. Only users who meet the attribute set can obtain decryption private key and decrypt the ciphertext data of the false information identification file.

[0068] By implementing access control through more flexible attribute management, data owners do not need to know the identities of potential authorized users and related key sets in advance, and can even keep the identities of authorized users anonymous in some scenarios.

[0069] For example, for the false information identification file ciphertext B1, its access structure tree is T1. The access user must meet three conditions at the same time, namely, belonging to Company A, having a position of M' and a senior level. Users who meet the access structure can use the identification file B1 to remove false information; Jim, an employee of Company A, has a position of M' and a senior level, and meets the access structure; Tom, an employee of Company A, has a position of M' and a mid-level level, and does not meet the access structure tree.

[0070] For the identification file ciphertext B2, its access structure tree is T2. The access user must meet two conditions at the same time, that is, he belongs to Company A and his position is M'. Users who meet this access structure can use the identification file B2 to remove false information; Jack, an employee of Company A, whose position is M', meets this access structure; Alice, an employee of Company A, whose position is N, does not meet this access structure tree.

[0071] For the identification file ciphertext B3, its access structure tree is T3. The access user must meet the conditions, that is, he belongs to Company A, or belongs to Company B' and has a position S. Users who meet the access structure can use the identification file B3 to remove false information; John, an employee of Company A, meets the access structure; Martin, an employee of Company B', whose position is I, does not meet the access structure; Smith, an employee of Company B', whose position is S, meets the access structure tree.

[0072] Access control technology based on attribute encryption implements access control through more flexible attribute management, that is, using the attribute set as the public key to encrypt data, requiring that only users who meet the attribute set can obtain the decryption private key and decrypt the data.

[0073] Step 5: Publish anonymous data. The data owner will eventually generate a unified anonymous data set for users to use. All users will face the same anonymous data set M.

[0074] B=B∪{B i},M=M∪{M i} (4)

[0075] B i and M i Keep adding to sets B and M.

[0076] For example, assuming that the trajectory segment s7 contains the user's real location, the initial set that only contains the real location is M = {s7}, which can be used as the anonymity level L0; if the initial privacy protection parameter k = 3 is set, and each level increases by k thereafter, the anonymity level L i The anonymous parameter is k i = k + (i-1) k = i·k, that is, k i =3+(i-1)·3=3i.

[0077] For anonymity level L1, the anonymous set formed is M1 = {s7, s8, s9}; for anonymity level L2, the anonymous set formed is M2 = {s7, s8, s9, s4, s5, s 11}; For anonymity level L3, the anonymous set formed is M3 = {s7,s8,s9,s4,s5,s 11 ,s2,s3,s 10}; The last published anonymous collection is M3.

[0078] Step 6: Encrypt the identification file and generate an attribute key;

[0079] Step 61: The data owner uses the public key PK to access the structure tree T i For identification file B i Encrypt and get the ciphertext CB i ;

[0080] Step 62: Then use the master key MK and the access structure tree T i Send it to a trusted third party (TTP), send the anonymous data set and the ciphertext set of the identification file to the data service provider (DSP), and store the data and attribute keys on different servers to prevent user privacy leakage;

[0081] Step 63: When user U iWhen you want to use more accurate anonymous data, send your own attribute certificate (CA, CertificateAttribute) to TTP to apply for the decryption private key SK of the corresponding identification file ciphertext;

[0082] Step 64: TTP accesses the structure tree T i , the user's attribute certificate CA i , generate the decryption private key Sk of the corresponding identification file ciphertext i Send to user U i ; U i Using Sk i Decrypt the ciphertext of the identification file CB i , get the false information mark B i , and then remove these false information from the anonymous data set to obtain relatively accurate data.

[0083] Use PK and access structure T to encrypt the identification file B into ciphertext CB:

[0084] CB=Encrypt(PK,T,B) (5)

[0085] Among them, Encrypt() is the encryption function.

[0086] Input security parameter λ to generate master key MK and public key PK:

[0087] (MK,PK)=Gen(λ) (6)

[0088] Among them, Gen() is the key generation function.

[0089] Use MK and user attribute value S to generate the user's private key SK:

[0090] SK=Gen(MK,S) (7)

[0091] For example, Jim, an employee of Company A, whose position is M' and whose level is senior, satisfies the access structure tree T1, and can obtain the decryption private key SK1 of the identification file ciphertext CB1; Tom, an employee of Company A, whose position is M' and whose level is intermediate, does not satisfy any access structure, and cannot obtain the decryption private key of the identification file.

[0092] Jack, an employee of Company A, whose position is M, satisfies the access structure tree T2, and can obtain the decryption private key SK2 of the identification file ciphertext CB2; Alice, an employee of Company A, whose position is N, does not satisfy any access structure, and cannot obtain the decryption private key of the identification file.

[0093] John, an employee of Company A, and Martin, an employee of Company B', whose position is I, satisfy the access structure tree T3 and can obtain the decryption private key SK3 of the identification file ciphertext CB3; Smith, an employee of Company B', whose position is S, does not satisfy any access structure and cannot obtain the decryption private key of the identification file.

[0094] Step 7: User access data; for ordinary users, since there is no key to decrypt the ciphertext of the identification file, only uniform high-anonymity data M can be used to better protect the user's privacy; for privileged users, different levels of decryption keys for the ciphertext of the identification file can be obtained from the trusted third-party TTP, and the de-anonymization key can be used to remove false information B to a certain extent. i , obtain relatively accurate position data M i , improve data utilization efficiency.

[0095] Use the private key SK to decrypt the ciphertext CB and obtain the false information identification file B; the Decrypt() operation can only succeed if the user attribute value S meets the access structure tree T.

[0096] B=Decrypt(CB,SK),M i =MB i (8)

[0097] Step 8: Privacy protection effect; mainly measured by location set entropy, location distance entropy, privacy protection efficiency and data usage efficiency. The calculation formulas are:

[0098] The formula for position set entropy is:

[0099]

[0100] Among them, p i Identify the similarity probability of user location points.

[0101] The formula for position distance entropy is:

[0102]

[0103] Among them, R i is the location point, R o is the center of the anonymous set.

[0104] The formula for privacy protection efficiency is:

[0105]

[0106] Among them, k ji is the position trajectory tr i privacy protection needs; is the average value of the privacy protection requirements of all location trajectories; j is the number of location trajectories in the set; p is the number of anonymous sets formed.

[0107] The formula for data usage efficiency is:

[0108]

[0109] Among them, d i represents the position l on the real trajectory i The corresponding position point l on the release trajectory i ', d represents the average distance between the corresponding position points on the real trajectory and the published trajectory; n represents the number of position points on the trajectory, and |L| represents the length of the trajectory.

[0110] The method of the present invention can support bidirectional, reversible and multi-level location privacy information protection based on attribute encryption, and can achieve different data accuracy when users with different trust levels use the same anonymous data set; the participants of the access control structure based on attribute encryption include data owners, trusted third parties, users, and service providers. The data owner has ownership of the data and shares the data with other users through the data service of the service provider; the data owner is responsible for formulating the access strategy (access structure T), executing the encryption algorithm to generate ciphertext data bound to the strategy, and then sending it to the service provider; the trusted third party is responsible for maintaining the corresponding relationship between the attributes and keys of each user, executing the key generation algorithm, generating public and secret parameters PK and MK for the data owner, and publishing attributes for the user. Key; it is the only participant in the entire access control system that needs to be fully trusted by other participants, but it is only responsible for sending keys and cannot access ciphertext data; the user is the visitor of the data. If the user has the attributes that meet the requirements of the policy bound to the ciphertext data, that is, holds the attribute key issued by a trusted third party for the corresponding attribute, then the decryption algorithm can be executed to obtain the data plaintext of the identification file, thereby realizing the de-anonymization and access to the data; the service provider is responsible for providing outsourced storage of data and providing users with various operation services on the data. It will honestly execute various operations initiated by users, but hopes to obtain more encrypted content; the present invention can realize the confidentiality of user privacy information, the multi-level privacy protection and the recoverability of anonymous data.

[0111] Attribute-based false information identification file encryption and decryption is the key to data recovery. Figure 3 This is a schematic diagram of the encrypted release and de-anonymization process of the identification file. The data owner will be the anonymous data set M and the series of identification file ciphertext CB i After being sent to the data server, the user accesses the data through the data server.

[0112] For ordinary users, they can obtain anonymous data set M and identification file ciphertext CB from the data service provider. i However, since they do not have the decryption key of the identification file ciphertext generated by the authority, they can only use the unified high-anonymity data M to better protect the privacy of users.

[0113] For privileged users, they can obtain anonymous data set M and identification file ciphertext CB from the data server. i , they obtain the keys SK for decrypting the ciphertext of the identification file at different levels from the authority i , can decrypt the corresponding identification file ciphertext and remove the false information marked in it. i , perform de-anonymization operations to obtain relatively accurate location data M i , improve the efficiency of data use.

[0114] The present invention adopts an access control strategy based on attribute encryption to encrypt the identification file, and determines the corresponding access structure according to the category of the identification file. Only users who meet the attribute conditions can decrypt the identification file and perform data de-anonymization. When the data owner needs to adjust the permissions of the privileged user, he only needs to modify the access structure tree and then re-encrypt the identification file, which reduces the time cost of the data owner to regenerate and distribute keys.

[0115] Verify data availability:

[0116] like Figure 4 As shown, it is a data availability effect diagram of the privacy protection method (BME-LPP) of the present invention under different privacy protection parameters; Figure 4 From the anonymized data availability of (a), we can see that the data utilization efficiency decreases with the increase of the privacy protection parameter k. This is because as the privacy protection parameter increases, the more false information is added, the greater the disturbance to the data set, and the lower the utilization efficiency of the data set. Figure 4 From the de-anonymized data availability in (b), it can be seen that the data utilization efficiency increases with the increase of the de-anonymization processing level; the de-anonymization level N is set to 5, the initial privacy protection parameter is set to k1=10, and the privacy protection parameter increment level for each anonymity level is 10, that is, k i =10+(i-1)10=10·i, (1≤i≤N); when the de-anonymization level is 1, the false information removed is relatively small, and the achieved data utilization efficiency is relatively low; as the de-anonymization level increases, more false information is removed, and the achieved data utilization rate continues to increase. When the de-anonymization level is 5, the achieved data utilization rate is the highest.

[0117] Verify the privacy protection effect:

[0118] like Figure 5 As shown in the figure, it is a comparison diagram of the privacy protection effect of the privacy protection method (BME-LPP) of the present invention and other related methods; it can be seen from the figure that the anonymity success rate decreases with the increase of k, because with the increase of the privacy protection parameter k, more and more false information needs to be included in the anonymous set, but within the specified spatial tolerance d, the difficulty of selecting a sufficient number of qualified locations is also increasing, so the anonymity success rate will decrease; the anonymity success rate achieved by the present invention is relatively high. On the one hand, by constructing a location adjacency table to select location points associated with the original location points to construct a false data set, the data features in the data set are kept consistent, and by adding location-related false information in a hierarchical manner, the user's real location information can be guaranteed not to be leaked; on the other hand, the de-anonymization identification file is encrypted and transmitted through attribute encryption, asymmetric encryption, identity authentication and other technologies, which can ensure that the user's location privacy information is not leaked.

[0119] Verify execution efficiency:

[0120] like Figure 6 As shown, it is a comparison chart of the execution efficiency of the privacy protection method (BME-LPP) of the present invention and other related methods. It can be seen from the figure that the computational overhead of all privacy protection methods increases with the increase of the privacy protection parameter k; because with the increase of the anonymity parameter k, more and more false information needs to be included in the anonymous set, and more and more qualified locations need to be selected, and the computational overhead of the system will continue to increase; the computational overhead of the present invention is relatively low, because the use of false information identification files to achieve de-anonymization processing is more direct and efficient, which reduces the computational overhead, and the formulation of access policies, generation of attribute keys, and encryption of identification files can be performed offline, which makes the computational overhead relatively small.

[0121] In order to solve the problems of uniform anonymity strength, permanent loss of data quality and inability to meet users' differentiated data usage needs caused by single-level, unidirectional, coarse-grained and irreversible privacy protection methods, the present invention proposes a bidirectional multi-layer reversible location privacy protection method based on attribute encryption based on application scenarios in which users with different trust levels access the same data resources, grant permissions when the user identity is unknown, and need to restore data after anonymization. The method can provide bidirectional, multi-level and fine-grained personalized privacy protection. On the one hand, by adding false information of different strengths in a hierarchical manner, bidirectional reversible processing of location privacy protection is realized, which includes both anonymized privacy protection and de-anonymized data refinement. According to user needs, multi-level privacy protection levels are set, and false information is added in a hierarchical manner, so that users with different permissions can finally obtain data of different anonymity levels and different precisions, thereby realizing multi-level and fine-grained protection of user privacy information. On the other hand, the access control method based on attribute encryption realizes resource control, solves the encryption management of de-anonymized identification files and the generation, distribution and management of attribute keys, realizes bidirectional reversible privacy protection, and has good privacy protection effect.

[0122] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A bidirectional and reversible multi-layer location privacy protection method based on attribute encryption, characterized in that: The following steps are involved: Step 1: Based on the user location trajectory dataset, construct a road segment undirected graph and adjacency table; use hash functions and random numbers to add false nodes; Step 2: Based on the adjacency list, anonymous data sets with several protection levels are constructed according to the anonymity level and corresponding configuration parameters; Step 3: Construct a de-anonymized false information identification file, and use the false information identification file to hierarchically mark the false information added at each anonymity level, so as to remove the corresponding false information during de-anonymization, and ensure the reversibility of anonymous data and the bidirectionality of the anonymization process; Step 4: formulate an access policy for the false information identification file according to the data owner, establish an access structure tree, use the attribute set as the public key to encrypt the data, and the user who meets the attribute set obtains the decryption private key and decrypts the ciphertext data of the false information identification file according to the private key; Step 5: The data owner generates a unified anonymous data set and provides it to users. All users face the same anonymous data set and publish it. Step 6: Encrypt the identification file and generate an attribute key; Step 7: Access the ciphertext of the identification file according to the user attributes; Step 8: Measure the effectiveness of privacy protection.

2. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 1 is characterized in that: The use of hash functions and random numbers to add fake nodes includes: Use pseudo-random numbers and hash functions to determine the false nodes in the adjacency list; Check the identification bit of a false node in the adjacency table. When the identification bit is 0, the false node is selected. When the identification bit is 1, the false node has a conflict, and a new false node is reselected using the open addressing method in the hash function.

3. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 1 is characterized in that: Step 2 specifically includes: Step 21: Set the anonymity level to L i Divide into N levels according to the order of anonymity, and calculate the anonymity parameter corresponding to a certain anonymity level; Step 22, starting from the anonymity level L1, construct an anonymous data set according to the configuration parameters (k1, t1, d1) of L1, where t1 is the time threshold and d1 is the distance threshold; start adding false nodes; select the anonymous parameter k1 position points directly connected to the position in L0 in the current adjacency table to construct the anonymous data set M1 of L1; Step 23: Based on L1, add fake nodes to M1 anonymity until the configuration parameters (k2, t2, d2) of L2 anonymity level are met, and the constructed anonymous set is M2; continue to iterate and construct L N-1 Anonymous dataset M N-1 .

4. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 3 is characterized in that: The formula for the anonymity parameter corresponding to a certain anonymity level is: k i =k1+(i-1)k1=i k1 (3) Among them, 1≤i≤N, k1 is the anonymous parameter corresponding to L1.

5. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 3 is characterized in that: When adding a fake node, a fake node is selected in the adjacency table by a random number until the configuration parameters of the anonymity level are met; If the user's anonymity condition is still not met after exceeding the distance threshold d1, the anonymity fails.

6. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 1 is characterized in that: The false information identification file corresponds to the set anonymity level, and the structure of the false information identification file is B=(ID, L, FID); wherein ID represents the serial number, L represents the anonymity level, and FID represents adding a false information identification.

7. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 1 is characterized in that: Step 6 specifically includes: Step 61: The data owner uses the public key PK to access the structure tree T i For identification file B i Encrypt and get the ciphertext CB i ; Step 62: Set the master key MK and the access structure tree T i Send to TTP; send the anonymous data set and the ciphertext set of the identification file to DSP; Step 63: User U i Attribute Certificate CA i Send to TTP to apply for the decryption private key SK of the corresponding identification file ciphertext; Step 64: TTP accesses the structure tree T i , Attribute Certificate CA i , generate the decryption private key Sk of the corresponding identification file ciphertext i Send to user U i ; U i Using Sk i Decrypt the ciphertext of the identification file CB i , get false information identification file B i , and then remove false information from the anonymous dataset to obtain accurate data.

8. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 1 is characterized in that: The user attributes for ordinary users use unified anonymous data to obtain location data, and the user attributes for privileged users use de-anonymization keys to remove false information and obtain accurate location data.

9. The bidirectional reversible multi-layer location privacy protection method based on attribute encryption according to claim 1 is characterized in that: Step 8 specifically includes: It is measured using location set entropy, location distance entropy, privacy protection efficiency, and data usage efficiency.

10. A bidirectional reversible multi-layer location privacy protection system based on attribute encryption, characterized in that: include: a memory for storing instructions executable by a processor; A processor, configured to execute instructions to implement the bidirectionally reversible multi-layer location privacy protection method based on attribute encryption as described in any one of claims 1-9.

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