Verifiable privacy protection multi-dimensional data aggregation method for Internet of Vehicles crowd sensing

Abstract

The invention belongs to the technical field of information security, and particularly relates to a verifiable privacy protection multi-dimensional data aggregation method, system and device for Internet of Vehicles crowd sensing, and a medium. The method comprises the steps that a trusted authority center initializes system parameters; the task vehicle performs data aggregation, confusion and secret segmentation on the privacy data according to the system parameters and generates a data report; the roadside unit performs secret reconstruction, batch verification and data aggregation on all the data reports according to the system parameters to generate an aggregation report; the traffic management center verifies and recovers the aggregation report according to the system parameters to obtain an aggregation result; the data requester verifies the final aggregation result based on the commitment value; according to the method, the calculation overhead required by the traditional encryption and decryption technology is avoided, the integrity authentication overhead in the data interaction process is reduced, the method supports multi-dimensional data aggregation, storage and verification, and finer-grained aggregation is realized.

Description

Technical Field

[0001] The present invention belongs to the field of information security technology, and specifically relates to a verifiable privacy-protected multidimensional data aggregation method for group intelligence perception of Internet of Vehicles. Background Art

[0002] As an emerging paradigm that can accomplish large-scale and complex social tasks, crowd perception has gradually been used by many scholars to form crowd perception in the Internet of Vehicles, so as to realize air quality monitoring, high-precision map drawing and real-time traffic system management, and promote the construction of smart cities and the development of intelligent transportation. With the increase in the amount of perception data collected by vehicles, the communication overhead of data transmission gradually increases. The use of data aggregation technology to compress perception data can effectively reduce communication overhead and improve data transmission efficiency.

[0003] In crowd sensing, data aggregation usually starts with the data requester issuing a task request to the traffic management center. The task-performing vehicle uploads the perception data to the roadside unit, which aggregates the data and uploads the result to the traffic management center. Finally, the traffic management center aggregates the data of all roadside units and sends the final aggregation result to the data requester. In this process, since the perception data collected by the task-performing vehicle contains private information such as vehicle identity, location (such as residential address, office location), and vehicle driving trajectory, and when the traffic management center and roadside unit are attacked by hackers, the traffic management center and roadside unit may become incompletely trusted entities. Directly aggregating and uploading the perception data will cause the task-performing vehicle to be at risk of privacy leakage. In addition, for the data requester, the aggregation result will also reflect the interests of the data requester. Once obtained by the adversary, the privacy of the data requester will be leaked, causing the data requester to refuse to accept the services provided by the traffic management center, thereby increasing the communication overhead during the transmission process. Therefore, how to deal with and solve the problems of data privacy protection and efficient and trusted aggregation in the crowd sensing of the Internet of Vehicles has attracted extensive attention from academia and industry at home and abroad.

[0004] The existing data aggregation methods in the prior art include, for example, a method for multidimensional data aggregation based on Paillier homomorphic encryption that can ensure the privacy of perceived data and system stability; and a multidimensional data aggregation algorithm that uses the Chinese remainder theorem to implement batch signature verification that can resist multiple attacks. The above solutions all use homomorphic encryption to convert multidimensional data into one-dimensional data and then encrypt and transmit it, thereby reducing the number of encryption times. However, batch verification is based on bilinear pairings, which has large computational and time overheads. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a privacy aggregation method, system, device and medium for Internet of Vehicles, the method comprising:

[0006] S1: The trusted authority initializes system parameters, including generating public and private keys for signature based on discrete logarithms, super-growth sequences for data aggregation, and blinding factors for data obfuscation;

[0007] S2: The mission vehicle aggregates, obfuscates and secretly segments the private data according to the system parameters and generates a data report;

[0008] S3: The roadside unit performs secret reconstruction, batch verification and data aggregation of all data reports according to system parameters to generate an aggregated report;

[0009] S4: The traffic management center verifies the aggregated report according to the system parameters and recovers the aggregated result;

[0010] S5: The data requester verifies the final aggregation result based on the committed value;

[0011] Preferably, step S1 comprises:

[0012] The trusted authority center selects the corresponding signature private keys for the mission vehicle and the roadside unit respectively, and calculates the signature public key corresponding to each private key;

[0013] Trusted authority center selects super growth sequence where a 1 ,a 2 ,…,a n is a large prime number and satisfies

[0014] The trusted authority center gives the security parameter k, selects two large prime numbers p and q, calculates N=pq, and randomly generates a tuple (G,g), where g is a random generator of the multiplicative cyclic group G. Then, a one-way hash function H is defined:

[0015] The trusted authority center selects random numbers for the mission vehicle As a blinding factor, a random number is selected for the roadside unit As a blinding factor, the traffic management center finally generates a blinding factor r c ,in

[0016] The trusted authority center sends the blinding factor of the traffic management center to the traffic management center through a secure channel, sends the blinding factor and signature private key of the roadside unit to the roadside unit, and sends the blinding factor and signature private key of the mission vehicle to the mission vehicle.

[0017] Preferably, step S2 comprises:

[0018] All mission vehicles jointly construct the report time point Τ = {t 1 ,t 2 ,…,tmax} and ID = {id 1 ,id 2 ,…,id n};

[0019] Use super-growth sequence to aggregate w kinds of sensory data to obtain one-dimensional data And perform obfuscation:

[0020] d i =m i +r i ,

[0021] Among them, r i represents the blinding factor of the mission vehicle;

[0022] Randomly generate a t-order polynomial function f i (x) = d i +b 1 x+…b t x t ,in The mission vehicle then calculates f i (id j ) and sent to the corresponding task vehicle, where id j ∈ ID;

[0023] Choose a random number β i ∈Z N ,calculate and h i =H(d i ||ID i ||t γ ) and generate the signature:

[0024] σ i =β i +h i *k i ,

[0025] Among them, k i Represents the signature private key of the mission vehicle;

[0026] Generate User Report {ID i ||t γ ||R i ||σ i} and sends it to the roadside unit.

[0027] Preferably, step S3 comprises:

[0028] Timestamp in roadside unit inspection user report i ;

[0029] After successful verification, the roadside unit reconstructs the perception data of the mission vehicle based on the received secret sub-share;

[0030] Roadside unit batch verification task vehicle signature;

[0031] After the signature is successfully verified, the roadside unit aggregates the data and introduces its own blinding factor to confuse the data, that is, calculate

[0032] Randomly generate beta w ∈Z N ,calculate and h w =H(D * ||t γ ) and sign the aggregated data

[0033] σ w =β w +h w *k w，

[0034] Among them, σ W Indicates the signature of the local gateway, ID GW Indicates the identity information of the local gateway, t γ Indicates the current timestamp, k w Indicates the private key of the local gateway;

[0035] The local gateway generates an aggregate report * ||t γ ||R w ||σ w} and send it to the traffic management center.

[0036] Preferably, step S4 comprises:

[0037] The traffic management center checks the timestamp t in the aggregate report γ , and verify the signature;

[0038] After the signature is successfully verified, the traffic management center recovers the obfuscated aggregate data based on its own blinding factor to obtain the aggregate data;

[0039] The traffic management center parses the decrypted plaintext overall aggregate data D based on the super-growth sequence.

[0040] Preferably, step S5 comprises:

[0041] The data requester receives the final aggregation result and obtains the commitment value and verification parameters from the blockchain. The verification process is as follows:

[0042]

[0043] If true, it means that the final aggregation result is correct and reliable, and the data requester accepts the obtained data; otherwise, the data requester terminates all programs.

[0044] 1. A lightweight, verifiable, privacy-preserving multidimensional data aggregation system for Internet of Vehicles crowd intelligence perception, characterized by comprising: a system initialization module, a user report generation module, an aggregation report generation module, a data recovery module, and an aggregation result verification module;

[0045] The system initialization module is used to initialize system parameters;

[0046] The user report generation module is used to generate a user report according to system parameters;

[0047] The aggregate report generation module is used to perform batch processing verification and data aggregation on user reports to generate an aggregate report;

[0048] The data recovery module is used to recover the aggregate report and obtain a recovery result;

[0049] The aggregation result verification module is used to verify the final aggregation result to ensure the correctness of the aggregation result.

[0050] A privacy aggregation device for the Internet of Vehicles, characterized in that it includes: a memory and a processor, and a privacy aggregation computer program stored in the memory and executable on the processor; when the privacy aggregation computer program is executed by the processor, a verifiable privacy-protected multidimensional data aggregation method for group intelligence perception of the Internet of Vehicles is implemented.

[0051] A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it can implement a verifiable privacy-preserving multidimensional data aggregation method for group intelligence perception of the Internet of Vehicles.

[0052] The beneficial effects of the present invention are as follows: by introducing a homomorphic commitment mechanism, the present invention can effectively prevent attacks from internal malicious nodes and enhance the system's ability to resist potential threats from aggregation nodes and cloud servers; a lightweight aggregation scheme combining Shamir secret sharing and blind factor obfuscation technology is proposed, which does not require encryption and decryption operations, thereby reducing the computational overhead in the data interaction process; the present invention has extremely low computational efficiency and is not easy to leak the privacy of mission vehicle perception data, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the privacy aggregation method for the Internet of Vehicles in the present invention;

[0054] Figure 2 This is a system model diagram of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The present invention proposes a privacy aggregation method, system, device and medium for Internet of Vehicles, such as Figure 1 , Figure 2 As shown, the method includes the following contents:

[0057] S1: The trusted authority initializes system parameters, including generating public and private keys for signature based on discrete logarithms, super-growth sequences for data aggregation, and blinding factors for data obfuscation.

[0058] System initialization:

[0059] The trusted authority center selects the corresponding signature private keys for the mission vehicle and the roadside unit respectively, and calculates the signature public key corresponding to each private key;

[0060] Trusted authority center selects super growth sequence where a 1 ,a 2 ,…,a n is a large prime number and satisfies

[0061] The trusted authority center gives the security parameter k, selects two large prime numbers p and q, calculates N=pq, and randomly generates a tuple (G,g), where g is a random generator of the multiplicative cyclic group G. Then, a one-way hash function H is defined:

[0062] The trusted authority center selects random numbers for the mission vehicle As a blinding factor, a random number is selected for the roadside unit As a blinding factor, the traffic management center finally generates a blinding factor r c ,in

[0063] The trusted authority center sends the blinding factor of the traffic management center to the traffic management center through a secure channel, sends the blinding factor and signature private key of the roadside unit to the roadside unit, and sends the blinding factor and signature private key of the mission vehicle to the mission vehicle.

[0064] S2: The task vehicle aggregates, obfuscates and secretly segments the private data according to the system parameters and generates a data report.

[0065] User report generation:

[0066] All mission vehicles jointly construct the report time point Τ = {t 1 ,t 2 ,…,t max} and ID = {id 1 ,id 2 ,…,id n};

[0067] The mission vehicle uses a super-growth sequence to aggregate w types of perception data to obtain one-dimensional data Then the blinding factor is introduced to obtain d i =m i +r i .

[0068] The mission vehicle randomly generates a t-order polynomial function f i (x) = d i +b 1 x+…b t x t ,in The mission vehicle then calculates f i (id j ) and sent to the corresponding other task vehicles, where (id j ∈ID).

[0069] Mission vehicle random generation β i ∈Z N ,calculate and h i =H(d i ||id i ||t γ ) and generate the signature:

[0070] σ w =β w +h w *k w ,

[0071] Mission vehicle package data report {id i ||t γ ||R i ||σ i} is sent to the roadside unit, and the secret sub-shares of other mission vehicles collected are sent to the roadside unit, where j∈(1,…,n),x i =id i .

[0072] S3: The roadside unit secretly reconstructs, batch-validates and aggregates all data reports according to system parameters to generate an aggregated report.

[0073] Aggregate report generation:

[0074] The roadside unit receives n data reports {id i ||t γ ‖R i ||σ i}, determine whether |t γ' -t γ |≤Δt, where t γ' is the current timestamp, and Δt is the preset time interval threshold.

[0075] The roadside unit randomly selects t secret sub-shares from the received secret sub-shares, that is, Among them, i∈(1,…,n), and then the mission vehicle data is secretly reconstructed:

[0076]

[0077] The roadside unit verifies the signature of the mission vehicle, that is, calculates and verifies the equation In order to reduce the computational and time overhead of verification, the roadside unit uses small index technology to implement batch verification. The roadside unit randomly selects a small index θ 1 ,θ 2 ,…,θ n ∈[1,2 n ], aggregate verification of mission vehicle signatures:

[0078]

[0079] The roadside unit aggregates the data and adds its own blinding factor r w :

[0080]

[0081] Roadside units randomly generate β w ∈Z N ,calculate and h w =H(D * ||t γ ) and sign the aggregate data:

[0082] σ w =β w +h w *k w

[0083] Roadside Unit Package Data Report {D * ||t γ ||R w ||σ w}Sent to the Traffic Management Center.

[0084] S4: The traffic management center verifies the aggregation report according to the system parameters and recovers the aggregation result.

[0085] Data Recovery:

[0086] The traffic management center determines whether the timestamp satisfies |t γ' -t γ |≤Δt, where t γ' is the current timestamp and is the preset time interval threshold.

[0087] Traffic Management Center's overall signature σ w To verify, that is, to calculate and verify the equation Is it true?

[0088] The traffic management center removes the blinding factor in the overall aggregated data:

[0089] D=D * +r c

[0090] The traffic management center parses the decrypted plaintext aggregate data D based on the super growth sequence to obtain multi-dimensional aggregate data M 1 ,M 2 ,…,M w ,in

[0091]

[0092] The traffic management center will multi-dimensional aggregate data (M 1 ,M 2 ,…,M w ) and the current timestamp t γ Store together.

[0093] S5: The data requester receives the final aggregation result and obtains the commitment value and verification parameters from the blockchain.

[0094] Aggregation result verification:

[0095] The data requester receives the final aggregation result and obtains the commitment value and verification parameters from the blockchain. The verification process is as follows:

[0096]

[0097] If true, it means that the final aggregation result is correct and reliable, and the data requester accepts the obtained data; otherwise, the data requester terminates all programs.

[0098] In some preferred embodiments of the present invention, the present invention also provides a lightweight verifiable privacy-preserving multidimensional data aggregation system for Internet of Vehicles crowd intelligence perception, including: a system initialization module, a user report generation module, an aggregation report generation module, a data recovery module and an aggregation result verification module;

[0099] The system initialization module is used to initialize system parameters;

[0100] The user report generation module is used to generate a user report according to system parameters;

[0101] The aggregate report generation module is used to perform batch processing verification and data aggregation on user reports to generate an aggregate report;

[0102] The data recovery module is used to recover the aggregate report and obtain a recovery result.

[0103] The aggregation result verification module is used to verify the final aggregation result to ensure the correctness of the aggregation result.

[0104] In some preferred embodiments of the present invention, a privacy aggregation device for the Internet of Vehicles is also provided, comprising: a memory and a processor, and a privacy aggregation computer program stored in the memory and executable on the processor; when the privacy aggregation computer program is executed by the processor, the above-mentioned privacy aggregation method for the Internet of Vehicles is implemented.

[0105] Furthermore, the processing device also includes at least one input device and at least one output device; in the processing device, the processor, the memory, the input device, and the output device are connected via a bus.

[0106] In some preferred embodiments of the present invention, the specific types of memory, input device and output device are not limited; for example, the input device can be a touch screen, an image acquisition device, a physical button or a mouse, etc.; the output device can be a display terminal. The memory can be a random access memory (RAMD) or a non-volatile memory (non-volatile memory), such as a disk storage.

[0107] In an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned privacy aggregation method for smart grids can be implemented.

[0108] In the embodiment of the present invention, the readable storage medium is a computer-readable storage medium and can be set in the aforementioned processing device, for example, as a memory in the processing device. In addition, the readable storage medium can also be a U disk, a mobile hard disk, a read-only memory (ROMD), a disk or an optical disk, and other media that can store program codes.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, including a number of instructions for a terminal device (which can be a mobile phone, computer, server, TV, or network device, etc.) to execute the method described in the embodiment of the present invention.

[0110] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the protection of the present invention.

Claims

1. A verifiable privacy-preserving multidimensional data aggregation method for Internet of Vehicles crowd intelligence perception, characterized in that: include: S1: The trusted authority initializes system parameters, including generating public and private keys for signature based on discrete logarithms, super-growth sequences for data aggregation, and blinding factors for data obfuscation; S2: The mission vehicle aggregates, obfuscates and secretly segments the private data according to the system parameters and generates a data report; S3: The roadside unit performs secret reconstruction, batch verification and data aggregation of all data reports according to system parameters to generate an aggregated report; S4: The traffic management center verifies the aggregated report according to the system parameters and recovers the aggregated result; S5: The data requester verifies the final aggregation result based on the committed value.

2. According to claim 1, a verifiable privacy-preserving multidimensional data aggregation method for Internet of Vehicles crowd intelligence perception is characterized in that: The step S1 comprises: The trusted authority center selects the corresponding signature private keys for the mission vehicle and the roadside unit respectively, and calculates the signature public key corresponding to each private key; Trusted authority center selects super growth sequence where a1, a2, …, a n is a large prime number and satisfies The trusted authority center gives the security parameter k, selects two large prime numbers p and q, calculates N=pq, and randomly generates a tuple (G, g), where g is a random generator of the multiplicative cyclic group G. Then, a one-way hash function H is defined: The trusted authority center selects random numbers for the mission vehicle As a blinding factor, a random number is selected for the roadside unit As a blinding factor, the traffic management center finally generates a blinding factor r c ,in The trusted authority center sends the blinding factor of the traffic management center to the traffic management center through a secure channel, sends the blinding factor and signature private key of the roadside unit to the roadside unit, and sends the blinding factor and signature private key of the mission vehicle to the mission vehicle.

3. The method for verifiable privacy-preserving multidimensional data aggregation for Internet of Vehicles crowd intelligence perception according to claim 1 is characterized in that: The step S2 comprises: All mission vehicles jointly construct the report time point Τ = {t1, t2, ..., t max } and mission vehicle ID = {id1, id2, ..., id n }; Use super-growth sequence to aggregate w kinds of sensory data to obtain one-dimensional data And perform obfuscation: d i =m i +r i , Among them, r i Represents the mission vehicle V i The blinding factor of Randomly generate a t-order polynomial function f i (x) = d i +b1x+…b t x t ,in Then the mission vehicle V i Calculate f i (id j ) and sent to the corresponding task vehicle, where id j ∈ ID; Choose a random number β i ∈Z N ,calculate and h i =H(d i ||ID i ||t γ ) and generate the signature: s i =b i +h i *k i , Among them, k i Represents the mission vehicle V i The signature private key of Generate user report {id i ||t γ ||R i ||σ i } and sends it to the roadside unit.

4. The method for verifiable privacy-preserving multidimensional data aggregation for Internet of Vehicles crowd intelligence perception according to claim 1 is characterized in that: The step S3 comprises: Timestamp in roadside unit inspection user report i ; After successful verification, the roadside unit reconstructs the perception data of the mission vehicle based on the received secret sub-share; Roadside unit batch verification task vehicle signature; After the signature is successfully verified, the roadside unit aggregates the data and introduces its own blinding factor to confuse the data, that is, calculate Randomly generate beta w ∈Z N ,calculate and h w =H(D * ||t γ ) and sign the aggregated data s w =b w +h w *k w， Among them, σ W Indicates the signature of the local gateway, ID GW Indicates the identity information of the local gateway, t γ Indicates the current timestamp, k w Indicates the private key of the local gateway; The local gateway generates an aggregate report * ||t γ ||R w ||σ w } and send it to the traffic management center.

5. The method for verifiable privacy-preserving multidimensional data aggregation for Internet of Vehicles crowd intelligence perception according to claim 1 is characterized in that: The step S4 comprises: The traffic management center checks the timestamp t in the aggregate report γ , and verify the signature; After the signature is successfully verified, the traffic management center recovers the obfuscated aggregate data based on its own blinding factor to obtain the aggregate data; The traffic management center parses the decrypted plaintext overall aggregate data D based on the super-growth sequence.

6. The method for verifiable privacy-preserving multidimensional data aggregation for Internet of Vehicles crowd intelligence perception according to claim 1 is characterized in that: The step S5 comprises: The data requester receives the final aggregation result and obtains the commitment value and verification parameters from the blockchain. The verification process is as follows: If true, it means that the final aggregation result is correct and reliable, and the data requester accepts the obtained data; otherwise, the data requester terminates all programs.

7. A lightweight, verifiable, privacy-preserving multidimensional data aggregation system for Internet of Vehicles crowd intelligence perception, characterized by: include: System initialization module, user report generation module, aggregation report generation module, data recovery module and aggregation result verification module; The system initialization module is used to initialize system parameters; The user report generating module is used to generate a user report according to the system parameters; The aggregate report generation module is used to perform batch verification and data aggregation on user reports to generate an aggregate report; The data recovery module is used to recover the aggregate report and obtain a recovery result; The aggregation result verification module is used to verify the final aggregation result to ensure the correctness of the aggregation result.

8. A privacy aggregation device for Internet of Vehicles, characterized in that: include: A memory and a processor, and a privacy aggregation computer program stored in the memory and executable on the processor; when the privacy aggregation computer program is executed by the processor, a verifiable privacy-preserving multidimensional data aggregation method for group intelligence perception of Internet of Vehicles as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, can implement a verifiable privacy-preserving multidimensional data aggregation method for group intelligence perception of the Internet of Vehicles as described in any one of claims 1 to 6.

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