Privacy-preserving communication and power injection over vehicle networks and 5G smart grid slice

• Main Authors: ZHANG, Yinghui, LI, Jin, ZHENG, Dong, LI, Ping, TIAN, Yangguang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Vehicle networks; Smart grid; Power injection; Security privacy; Aggregation; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5307; https://ink.library.smu.edu.sg/context/sis_research/article/6310/viewcontent/Privacy_Preserving_Comm_5G_av.pdf
• Institution: Singapore Management University

As an important combination of autonomous vehicle networks (AVNs) and smart grid, the vehicle-to-grid (V2G) network can facilitate the adoption of renewable resources. Based on V2G networks, parked electric vehicles (EVs) can charge during off-peak hours and inject excess power to the grid during peak hours for earnings. However, each EV's power injection bids in V2G are sensitive and vehicle-to-vehicle (V2V) communication may be eavesdropped, which has become an obstacle to the wide deployments of AVNs. Aiming to efficiently tackle these security and privacy issues in AVNs, we propose an efficient privacy-preserving communication and power injection (ePPCP) scheme without pairings, which is suitable for vehicle networks and 5G smart grid slice. In ePPCP, each EV calculates two secret keys shared respectively by the utility company and the gateway to blind power injection bids. A novel aggregation technique called hash-then-homomorphic is used to further aggregate the blinded bids of different time slots. Our security analysis indicates that individual bids are hidden and secure V2V communication is ensured. Furthermore, extensive performance comparisons show that ePPCP is efficient in terms of the computation cost and communication overhead.