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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Vincent Drury (IT-Security Research Group, RWTH Aachen University), Rene Roepke (Learning Technologies Research Group, RWTH Aachen University), Ulrik Schroeder (Learning Technologies Research Group, RWTH Aachen University), Ulrike Meyer (IT-Security Research Group, RWTH Aachen University)

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Interpretable Federated Transformer Log Learning for Cloud Threat Forensics

Gonzalo De La Torre Parra (University of the Incarnate Word, TX, USA), Luis Selvera (Secure AI and Autonomy Lab, The University of Texas at San Antonio, TX, USA), Joseph Khoury (The Cyber Center For Security and Analytics, University of Texas at San Antonio, TX, USA), Hector Irizarry (Raytheon, USA), Elias Bou-Harb (The Cyber Center For…

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The Impact of Workload on Phishing Susceptibility: An Experiment

Sijie Zhuo (University of Auckland), Robert Biddle (University of Auckland and Carleton University, Ottawa), Lucas Betts, Nalin Asanka Gamagedara Arachchilage, Yun Sing Koh, Danielle Lottridge, Giovanni Russello (University of Auckland)

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