PAPU: Pseudonym Swap With Provable Unlinkability Based on Differential Privacy in VANETs

Li, Xinghua; Zhang, Huijuan; Ren, Yanbing; Ma, Siqi; Luo, Bin; Weng, Jian; Ma, Jianfeng; Huang, Xiaoming

doi:10.1109/jiot.2020.3001381

Cited by 52 publications

(16 citation statements)

References 39 publications

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“…At each pseudonym exchange, RSU selected two vehicles at random to switch, and it informed TA of the outcome so that the pseudonym mapping could be updated. Li et al [21] strengthened the unlinkability of pseudonyms and increased the constraints for choosing vehicles to interchange pseudonyms by applying differential privacy.…”

Section: Related Workmentioning

confidence: 99%

An Anonymous Signature-Based Authentication and Key Agreement Scheme for Vehicular Ad Hoc Networks

Azees

Rajasekaran

Satti

2022

Security and Communication Networks

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Anonymous authentication is a critical step in safeguarding vehicle privacy and security in VANETs. VANETs connected with blockchain are gaining popularity as a means to increase the effectiveness of anonymous authentication across many security domains. However, present blockchain-assisted authentication systems cannot successfully achieve anonymity since colluding RSUs or vehicles may acquire linkability via the same retrieved record, hence destroying anonymity. To solve the problem, the proposed work offers an unlinkable anonymous signature-based authentication for VANET to ensure collusion resistance. To provide V2R unlinkability, a trusted authority issues anonymous parameters that conceal the vehicle's identification from RSUs and other vehicles in the VANET system. The vehicle user produces anonymous signatures, and RSUs validate them during anonymous authentication. Moreover, the proposed authentication methods are based on an anonymous certificateless signature (ACS) approach that is computationally more efficient and provably safe against eternal forgery in the random oracle model. Additionally, the proposed work guarantees that neither an RSU nor a vehicle has the authority to divulge users’ true identities. Hence, the proposed system has stringent unlinkability and better anonymity, and it enhances the efficiency of V2R and V2V communications considerably according to security analysis and performance assessment.

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Section: Related Workmentioning

confidence: 99%

An Anonymous Signature-Based Authentication and Key Agreement Scheme for Vehicular Ad Hoc Networks

Azees

Rajasekaran

Satti

2022

Security and Communication Networks

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“…The work of [4] studied unlinkability based on differential privacy in Vehicle Ad-Hoc Networks (VANETS). Hereby, a vehicle may communicate with other vehicles or road infrastructure.…”

Section: Literature Review Resultsmentioning

confidence: 99%

“…A more recent improvement of the model is also able to replace license plate information and biometric data in videos. 4 Although the image generator model does not see any personal data, such a solution could be run in a TEE within the vehicle. Thus, personal data would not be stored in the vehicle and could not be transferred to other entities such as the B-IP.…”

Section: Vehicle Processing Capabilities Pedestrian Density and Background Pedestrian Poses Occlusionmentioning

confidence: 99%

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Rannenberg¹,

Pape²,

Tronnier³

et al. 2021

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The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data. For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.

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“…To address the privacy issues in VANET, various researchers tried to solve the issues from various research angles. Existing privacy frameworks: the differential privacy framework [145], its extensions [146], and the classic privacy framework are not sufficient to solve the privacy issues in VANET. To date, researchers did not find an optimal global solu-tion for data privacy, and utility protection [147].…”

Section: Federated Learning and Blockchain For Privacy Preservation I...mentioning

confidence: 99%

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

Javed

Hassan

Shahzad

et al. 2022

Sensors

106

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The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.

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PAPU: Pseudonym Swap With Provable Unlinkability Based on Differential Privacy in VANETs

Cited by 52 publications

References 39 publications

An Anonymous Signature-Based Authentication and Key Agreement Scheme for Vehicular Ad Hoc Networks

An Anonymous Signature-Based Authentication and Key Agreement Scheme for Vehicular Ad Hoc Networks

Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

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