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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From Library Portability to Para-rehosting: Natively Executing Microcontroller Software...

Wenqiang Li (State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences; Department of Computer Science, the University of Georgia, USA; School of Cyber Security, University of Chinese Academy of Sciences; Department of Electrical Engineering and Computer Science, the University of Kansas, USA), Le Guan (Department of Computer Science, the University…

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The Bluetooth CYBORG: Analysis of the Full Human-Machine Passkey...

Michael Troncoso (Naval Postgraduate School), Britta Hale (Naval Postgraduate School)

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XDA: Accurate, Robust Disassembly with Transfer Learning

Kexin Pei (Columbia University), Jonas Guan (University of Toronto), David Williams-King (Columbia University), Junfeng Yang (Columbia University), Suman Jana (Columbia University)

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Differentially Private Health Tokens for Estimating COVID-19 Risk

David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)

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