CN115348280A - Vehicle networking data sharing method based on trusted block chain - Google Patents
Vehicle networking data sharing method based on trusted block chain Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0407—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the identity of one or more communicating identities is hidden
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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Abstract
The invention relates to a vehicle networking data sharing method based on a trusted block chain, and belongs to the technical field of vehicle networking data safety sharing. On one hand, the Laplace algorithm is reconstructed by a random response mechanism to meet the requirement of the localized differential privacy, the position data of the vehicle is disturbed by adopting the reconstructed localized differential privacy technology, and the original position data of each vehicle user is disturbed from the vehicle client; and on the other hand, the data transmitted to the data acquisition center is clustered by using a K-means clustering algorithm, so that the position information of the vehicle user is protected from being leaked while the safe and credible sharing of the data is realized. The invention has the advantages of high safety, high reliability and high efficiency.
Description
Technical Field
The invention relates to a vehicle networking data sharing method based on a trusted block chain, and belongs to the technical field of vehicle networking data safety sharing.
Background
With the popularization of automobiles and the rapid development of the internet of things, the concept of internet of vehicles has become one of the most popular subjects today. The vehicle networking is a distributed network for realizing data sharing interaction between vehicle-vehicle, vehicle-road-information and vehicle-network platforms according to a unified communication protocol and a data interaction standard. A large number of vehicle-mounted sensors are mounted on vehicles in the Internet of vehicles, information such as various roads, traffic, weather and the like in the driving process of the vehicles can be collected in real time, and the vehicle processor shares information with other vehicles in a wireless communication mode after screening the data.
However, while the internet of vehicles is rapidly developed, a series of problems such as data leakage, data tampering, data credibility and the like are brought. The reasons for such problems in the internet of vehicles are mainly three: 1) The vehicle terminal is maliciously attacked in the data acquisition process to cause data leakage; 2) Trust issues in the vehicle data sharing process; 3) False information may be uploaded in the internet of vehicles because the user identity is not public.
At present, researchers at home and abroad have various models and methods for protecting the safety of the data of the Internet of vehicles, wherein the problems of data privacy, data credibility and the like in the Internet of vehicles are solved by utilizing the characteristics of a block chain technology, and the defects still exist.
Disclosure of Invention
The invention aims to provide a vehicle networking data sharing method based on a trusted block chain, which is used for solving the safety problem in the vehicle networking data sharing process.
The technical scheme of the invention is as follows: a vehicle networking data sharing method based on a trusted block chain comprises the following specific steps:
step1: the vehicle sensors collect weather, road, traffic condition information of the current position of the vehicle.
Step2: and the vehicle client operates a random response algorithm to generate privacy parameters of the Laplace algorithm.
Step3: and the vehicle client operates a Laplace algorithm, calls the privacy parameters generated by the random response algorithm, calculates noise, and then adds the noise to the position data to generate disturbed non-original data.
Step4: and the vehicle client sends the disturbed non-original data to a data acquisition center, and the data acquisition center executes a K-means clustering algorithm to cluster all data in the area according to the position data.
Step5: the data acquisition center transmits the center point of each cluster and all the data in the cluster to the blockchain SDK.
Step6: and the block chain SDK calls an intelligent contract to be stored in a block in the block chain network after the data in each cluster is subjected to noise reduction.
In Step2, the privacy parameter is calculated by the following formula:
where Δ f is a privacy parameter and p is a perturbation probability.
In Step3, the noise is calculated by the following formula:
wherein f (X) is original position data,
for added Laplace noise, ε isA privacy budget.
Noise is added into the position data of the vehicle user, so that the attack difficulty of a malicious attacker can be increased, and the position safety of the user is protected while the position privacy of the vehicle is protected.
Step4 comprises the following specific steps:
step4.1: initializing cluster number k, iteration number N, and using position data sent by a user as a data set X = { X = (X) } 1 ,x 2 ,...,x n }。
Step4.2: calculating k initialization center points mu j ，j＝1,2,...,k。
Step4.3: calculating the distance D of each point in the data set D from the center point ij ＝||x i -μ j || 2 ，i＝1,2,...,n。
Step4.4: for distance d ij And sorting and carrying out primary clustering.
Step4.5: and re-counting new clustering centers, and circularly executing step4.2 to step4.4 until the clustering result is the same as the last clustering result.
Step4.6: finally, the cluster classification C = { C is obtained 1 ,C 2 ,...,C k }。
The invention has the beneficial effects that:
1. disturbance noise is added into the vehicle position data by using a localized differential privacy technology, so that the difficulty of attack of a malicious attacker is increased, the vehicle position privacy is protected, and the position safety of a user is protected;
2. and the data are clustered and uploaded to the block chain network by using a K-means clustering algorithm, so that the data query rate of a data consumer can be improved.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
FIG. 2 is a diagram of an architecture for trusted shared secure transmission of Internet of vehicles data in an embodiment of the present invention;
FIG. 3 is an error diagram of the centroid for the method of the present invention at different privacy budgets.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Example 1: as shown in fig. 2, the invention provides a vehicle networking data sharing method based on a trusted block chain, which comprises three layers of structures, namely a vehicle intelligent terminal, a data acquisition center and a block chain network, wherein trusted data is transmitted in 3 layers of an intelligent vehicle networking system, and each layer of data is operated to realize different functions. The vehicle intelligent terminal collects data in the real world through a vehicle sensor, then encrypts the collected data (except position data), adds noise to the position data by using a localized differential privacy technology to disturb the original data into non-original data, then sends the disturbed non-original data to a data acquisition center, the data acquisition center clusters the collected data and then sends the clustered data to a block chain network, and a data consumer can inquire all information of the current position on the block chain network.
The block chain has the technical characteristics of data traceability, difficult tampering, openness and transparency, automatic execution of intelligent contracts and the like, and is a profit for solving the problems of multi-party collaboration and multi-party trust. A trust foundation is established between the participation prevention through a consensus mechanism, and point-to-point value transfer is realized. And the authenticity verification and audit of the data on the chain are realized through an intelligent contract. And through the setting and consensus of a cooperation mechanism and an incentive mechanism, the data open sharing and value cooperation are promoted.
Differential privacy is achieved by adding specifically distributed noise to source data or computer data to ensure that each participant cannot analyze that the data set contains a specific entity through the obtained data.
As shown in fig. 1, the specific steps are as follows:
step1: the vehicle sensors collect weather, road, traffic condition information of the current position of the vehicle.
Step2: and the vehicle client operates a random response algorithm to generate privacy parameters of the Laplace algorithm.
Step3: and the vehicle client operates a Laplace algorithm, calls the privacy parameters generated by the random response algorithm, calculates noise, and then adds the noise to the position data to generate disturbed non-original data.
Step4: and the vehicle client sends the disturbed non-original data to a data acquisition center, and the data acquisition center executes a K-means clustering algorithm to cluster all data in the area according to the position data.
Step5: the data acquisition center transmits the center point of each cluster and all the data in the cluster to the blockchain SDK.
Step6: and the block chain SDK calls an intelligent contract to be stored in a block in the block chain network after the data in each cluster is subjected to noise reduction.
In Step2, the privacy parameter is calculated by the following formula:
where Δ f is a privacy parameter and p is a perturbation probability.
In Step3, the noise is calculated by the following equation:
wherein f (X) is original position data,
for added Laplace noise, ε is the privacy budget.
Step4 comprises the following specific steps:
step4.1: initializing cluster number k, iteration number N, and using position data sent by a user as a data set X = { X = (X) } 1 ,x 2 ,...,x n }。
Step4.2: calculating k initialization center points mu j ，j＝1,2,...,k。
Step4.3: calculating the distance D of each point in the data set D from the center point ij ＝||x i -μ j || 2 ，i＝1,2,...,n。
Step4.4: for distance d ij And sorting and carrying out primary clustering.
Step4.5: and re-counting new clustering centers, and circularly executing step4.2 to step4.4 until the clustering result is the same as the last clustering result.
Step4.6, finally obtaining cluster classification C = { C = { C 1 ,C 2 ,...,C k }。
Fig. 3 shows an error diagram of the centroid of the method according to the invention at different privacy budgets.
And measuring the error of the experimental result by adopting the K-means clustering centroid distance Dis before and after the experiment. The smaller the centroid change before and after the experiment, the smaller the difference between the ranges of the data set division before and after the position protection is proved to be, the closer the real result is. Before the LDP algorithm provided by the invention is used, K-means clustering is carried out on initial real data, and the clustered centroid uses C k And (4) showing. After the LDP algorithm is used, the noise-added data are subjected to K-means clustering, and the clustered centroids are C' k And (4) showing. And calculating the Euclidean distance Dis of the successive centroids under different privacy parameters epsilon, wherein the smaller the distance is, the smaller the data error is, and the higher the similarity is.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit and scope of the present invention.
Claims (4)
1. A vehicle networking data sharing method based on a trusted block chain is characterized in that:
step1: collecting weather, road and traffic condition information of the current position of a vehicle by a vehicle sensor;
step2: the vehicle client operates a random response algorithm to generate privacy parameters of a Laplace algorithm;
step3: the vehicle client operates a Laplace algorithm, calls a privacy parameter generated by a random response algorithm, calculates noise, and then adds the noise to the position data to generate disturbed non-original data;
step4: the vehicle client sends the disturbed non-original data to a data acquisition center, and the data acquisition center executes a K-means clustering algorithm to cluster all data in the area according to the position data;
step5: the data acquisition center transmits the central point of each cluster and all data in the cluster to the block chain SDK;
step6: and the block chain SDK calls an intelligent contract to be stored in a block in the block chain network after the data in each cluster is subjected to noise reduction.
2. The method for sharing data in the internet of vehicles based on the trusted block chain as claimed in claim 1, wherein in Step2, the privacy parameter is calculated by the following formula:
where Δ f is a privacy parameter and p is a perturbation probability.
3. The method for sharing data in the internet of vehicles based on the trusted block chain as claimed in claim 1, wherein in Step3, the noise is calculated by the following formula:
wherein f (X) is original position data,
for added Laplace noise, ε is the privacy budget.
4. The method for sharing data in the internet of vehicles based on the trusted block chain as claimed in claim 1, wherein Step4 comprises the following specific steps:
step4.1: initializing cluster number k, iteration number N, and using position data sent by a user as a data set X = { X = (X) } 1 ,x 2 ,...,x n }；
Step4.2: calculating k initialization center points mu j ，j＝1,2,...,k；
Step4.3: calculating the distance D of each point in the data set D from the center point ij ＝||x i -μ j || 2 ，i＝1,2,...,n；
Step4.4: for distance d ij Sorting and carrying out primary clustering;
step4.5: re-calculating new clustering centers, and circularly executing step4.2 to step4.4 until the clustering result is the same as the last clustering result;
step4.6: finally, the cluster classification C = { C is obtained 1 ,C 2 ,...,C k }。
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Citations (5)
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| CN112767693A (en) * | 2020-12-31 | 2021-05-07 | 北京明朝万达科技股份有限公司 | Vehicle driving data processing method and device |
| CN113537308A (en) * | 2021-06-29 | 2021-10-22 | 中国海洋大学 | Two-stage k-means clustering processing system and method based on localized differential privacy |
| CN113609523A (en) * | 2021-07-29 | 2021-11-05 | 南京邮电大学 | Vehicle networking private data protection method based on block chain and differential privacy |
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- 2022-06-27 CN CN202210737274.3A patent/CN115348280A/en active Pending
Patent Citations (5)
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| CN106911670A (en) * | 2017-01-13 | 2017-06-30 | 重庆邮电大学 | Intimacy protection system and method in a kind of car networking |
| CN112559593A (en) * | 2020-12-14 | 2021-03-26 | 长安大学 | Localized differential privacy protection method based on label clustering |
| CN112767693A (en) * | 2020-12-31 | 2021-05-07 | 北京明朝万达科技股份有限公司 | Vehicle driving data processing method and device |
| CN113537308A (en) * | 2021-06-29 | 2021-10-22 | 中国海洋大学 | Two-stage k-means clustering processing system and method based on localized differential privacy |
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