Andreas Unterweger, Fabian Knirsch, Clemens Brunner and Dominik Engel (Center for Secure Energy Informatics, Salzburg University of Applied Sciences, Puch bei Hallein, Austria)

The increasing amount of electric vehicles and a growing electric vehicle ecosystem is becoming a highly heterogeneous environment with a large number of participants that interact and communicate. Finding a charging station, performing vehicle-to-vehicle charging or processing payments poses privacy threats to customers as their location and habits can be traced. In this paper, we present a privacy-preserving solution for grid-to-vehicle charging, vehicle-to-grid charging and vehicle to-vehicle charging, that allows for finding the right charging option in a competitive market environment and that allows for built-in payments with adjustable and limited risk for both, producers and consumers of electricity. The proposed approach builds on blockchain technology and extends a state-of-the-art protocol with payments, while still preserving the privacy of the users. The protocol is evaluated with respect to privacy, risk and scalability. It is shown that pseudonymity and location privacy (against third parties) is guaranteed throughout the protocol, even beyond a single protocol session. In addition, both, risk and scalability can be adjusted based on the used blockchain.

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LaKSA: A Probabilistic Proof-of-Stake Protocol

Daniel Reijsbergen (Singapore University of Technology and Design), Pawel Szalachowski (Singapore University of Technology and Design), Junming Ke (University of Tartu), Zengpeng Li (Singapore University of Technology and Design), Jianying Zhou (Singapore University of Technology and Design)

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Towards Defeating Mass Surveillance and SARS-CoV-2: The Pronto-C2 Fully...

Gennaro Avitabile, Vincenzo Botta, Vincenzo Iovino, and Ivan Visconti (University of Salerno)

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Effects of Precise and Imprecise Value-Set Analysis (VSA) Information...

Laura Matzen, Michelle A Leger, Geoffrey Reedy (Sandia National Laboratories)

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(Short) WIP: End-to-End Analysis of Adversarial Attacks to Automated...

Hengyi Liang, Ruochen Jiao (Northwestern University), Takami Sato, Junjie Shen, Qi Alfred Chen (UC Irvine), and Qi Zhu (Northwestern University) Best Short Paper Award Winner!

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