Zekun Cai (Penn State University), Aiping Xiong (Penn State University)

To enhance the acceptance of connected autonomous vehicles (CAVs) and facilitate designs to protect people’s privacy, it is essential to evaluate how people perceive the data collection and use inside and outside the CAVs and investigate effective ways to help them make informed privacy decisions. We conducted an online survey (N = 381) examining participants’ utility-privacy tradeoff and data-sharing decisions in different CAV scenarios. Interventions that may encourage safer data-sharing decisions were also evaluated relative to a control. Results showed that the feedback intervention was effective in enhancing participants’ knowledge of possible inferences of personal information in the CAV scenarios. Consequently, it helped participants make more conservative data-sharing decisions. We also measured participants’ prior experience with connectivity and driver-assistance technologies and obtained its influence on their privacy decisions. We discuss the implications of the results for usable privacy design for CAVs.

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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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Kasper: Scanning for Generalized Transient Execution Gadgets in the...

Brian Johannesmeyer (VU Amsterdam), Jakob Koschel (VU Amsterdam), Kaveh Razavi (ETH Zurich), Herbert Bos (VU Amsterdam), Cristiano Giuffrida (VU Amsterdam)

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Cross-Language Attacks

Samuel Mergendahl (MIT Lincoln Laboratory), Nathan Burow (MIT Lincoln Laboratory), Hamed Okhravi (MIT Lincoln Laboratory)

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