Zihao Shen , Yuanjie Wang , Hui Wang , Peiqian Liu , Kun Liu , Mengke Liu
{"title":"车联网中基于区块链和机器学习的隐私保护预测缓存方法","authors":"Zihao Shen , Yuanjie Wang , Hui Wang , Peiqian Liu , Kun Liu , Mengke Liu","doi":"10.1016/j.vehcom.2024.100771","DOIUrl":null,"url":null,"abstract":"<div><p>To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.</p></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":null,"pages":null},"PeriodicalIF":5.8000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Privacy-protecting predictive cache method based on blockchain and machine learning in Internet of vehicles\",\"authors\":\"Zihao Shen , Yuanjie Wang , Hui Wang , Peiqian Liu , Kun Liu , Mengke Liu\",\"doi\":\"10.1016/j.vehcom.2024.100771\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.</p></div>\",\"PeriodicalId\":54346,\"journal\":{\"name\":\"Vehicular Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vehicular Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214209624000469\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vehicular Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214209624000469","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Privacy-protecting predictive cache method based on blockchain and machine learning in Internet of vehicles
To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.
期刊介绍:
Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Advances in wireless communications are making possible sharing of information through real time communications between vehicles and infrastructure. This has led to applications to increase safety of vehicles and communication between passengers and the Internet. Standardization efforts on vehicular communication are also underway to make vehicular transportation safer, greener and easier.
The aim of the journal is to publish high quality peer–reviewed papers in the area of vehicular communications. The scope encompasses all types of communications involving vehicles, including vehicle–to–vehicle and vehicle–to–infrastructure. The scope includes (but not limited to) the following topics related to vehicular communications:
Vehicle to vehicle and vehicle to infrastructure communications
Channel modelling, modulating and coding
Congestion Control and scalability issues
Protocol design, testing and verification
Routing in vehicular networks
Security issues and countermeasures
Deployment and field testing
Reducing energy consumption and enhancing safety of vehicles
Wireless in–car networks
Data collection and dissemination methods
Mobility and handover issues
Safety and driver assistance applications
UAV
Underwater communications
Autonomous cooperative driving
Social networks
Internet of vehicles
Standardization of protocols.