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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PHOENIX: Device-Centric Cellular Network Protocol Monitoring using Runtime Verification

Mitziu Echeverria (The University of Iowa), Zeeshan Ahmed (The University of Iowa), Bincheng Wang (The University of Iowa), M. Fareed Arif (The University of Iowa), Syed Rafiul Hussain (Pennsylvania State University), Omar Chowdhury (The University of Iowa)

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Your Phone is My Proxy: Detecting and Understanding Mobile...

Xianghang Mi (University at Buffalo), Siyuan Tang (Indiana University Bloomington), Zhengyi Li (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Feng Qian (University of Minnesota Twin Cities), XiaoFeng Wang (Indiana University Bloomington)

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Tales of Favicons and Caches: Persistent Tracking in Modern...

Konstantinos Solomos (University of Illinois at Chicago), John Kristoff (University of Illinois at Chicago), Chris Kanich (University of Illinois at Chicago), Jason Polakis (University of Illinois at Chicago)

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FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data

Junjie Liang (The Pennsylvania State University), Wenbo Guo (The Pennsylvania State University), Tongbo Luo (Robinhood), Vasant Honavar (The Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign), Xinyu Xing (The Pennsylvania State University)

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