Mridula Singh (CISPA - Helmholtz Center for Information Security), Marc Roeschlin (ETH Zurich), Aanjhan Ranganathan (Northeastern University), Srdjan Capkun (ETH Zurich)

A number of safety- and security-critical applications such as asset tracking, smart ecosystems, autonomous vehicles and driver assistance functions, etc., are expected to benefit from the position information available through 5G.
Driven by the aim to support such a wide-array of location-aware services and applications, the current release of 5G seeks to explore ranging and positioning [1] as an integral part of 5G technology. In recent years, many attacks on positioning and ranging systems have been demonstrated, and hence it is important to build 5G systems that are resilient to distance and location manipulation attacks. No existing proposal either by 3GPP or the research community addresses the challenges of secure position estimation in 5G.
In this paper, we develop V-Range, the first secure ranging system that is fully compatible with 5G standards and can be implemented directly on top of existing 5G-NR transceivers.
We design V-Range, a system capable of executing secure ranging operations resilient to both distance enlargement and reduction attacks.

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Hybrid Trust Multi-party Computation with Trusted Execution Environment

Pengfei Wu (School of Computing, National University of Singapore), Jianting Ning (College of Computer and Cyber Security, Fujian Normal University; Institute of Information Engineering, Chinese Academy of Sciences), Jiamin Shen (School of Computing, National University of Singapore), Hongbing Wang (School of Computing, National University of Singapore), Ee-Chien Chang (School of Computing, National University of Singapore)

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WIP: On Robustness of Lane Detection Models to Physical-World...

Takami Sato (UC Irvine) and Qi Alfred Chen (UC Irvine)

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Fine-Grained Coverage-Based Fuzzing

Bernard Nongpoh (Université Paris Saclay), Marwan Nour (Université Paris Saclay), Michaël Marcozzi (Université Paris Saclay), Sébastien Bardin (Université Paris Saclay)

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MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University); Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

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