Privacy Preserving Distributed Stable Matching of Electric Vehicles and Charge Suppliers

The potential of electric vehicles (EV) to reduce foreign-oil dependence and improve urban air quality has triggered lots of investment by automotive companies recently and mass penetration and market dominance of EVs is imminent. However, EVs need to be charged more frequently than fossil-based vehicles and the charging durations are much longer. This necessitates in advance scheduling and matching depending on the route of the EVs. However, such scheduling and frequent charging may leak sensitive information about the users which may expose their driving patterns, whereabouts, schedules, etc. The situation is compounded with the proliferation of EV chargers such as V2V charging where there can be a lot of privacy exposure if matching of suppliers and EVs is achieved in a centralized manner. To address this issue, in this paper, we propose a privacy-preserving distributed stable matching of EVs with suppliers (i.e., public/private stations, V2V chargers) using preference lists formed by partially homomorphic encryption-based distance calculations while hiding the locations. The simulation results indicate that such a local matching of supplier and demanders can be achieved in a distributed fashion within reasonable computation and convergence times while preserving privacy of users.