
With the number of Electric Vehicles (EVs) increasing, there is a growing need to schedule the charging of EVs in order to optimize grid load and improve stability. Charging scheduling involves several communications between the EV and the charging station (state of charge and time of arrival) happening either immediately before charging or at some earlier point in time. On account of privacy concerns and the potential for tracking of the users and their behaviors, the reporting of information from the EV to the charging station needs to be anonymous. Communications also need to be authenticated by the charging station to defend against external attacks. Additionally, this anonymity should be revocable to identify malicious, misbehaving users. In this paper, we leverage federated trust management and propose a revocable anonymous authentication framework that achieves the above desirable functionalities for reporting and interaction between an EV and the charging station. Using our framework, an EV can communicate with the charging station using anonymous pseudonyms that cannot be linked to its identity by the charging station alone. If the EV's anonymity needs to be revoked, the charging station can identify the EV with the help of the federated trust entities. We also present a proof mechanism that an EV can use to prove it did not misbehave. Security analysis and experiments demonstrate that our framework is secure, robust, scalable, and light-weight.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 15 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
