Habiba Farzand (University of Glasgow), Florian Mathis (University of Glasgow), Karola Marky (University of Glasgow), Mohamed Khamis (University of Glasgow)

Contact Tracing Apps (CTAs) have been developed and deployed in various parts of the world to track the spread of COVID-19. However, low social acceptance and the lack of adoption can impact CTA effectiveness. Prior work primarily focused on the privacy and security of CTAs, compared different models, and studied their app design. However, it remains unclear (1) how CTA privacy is perceived by end-users; (2) what reasons behind low adoption rates are, and (3) what the situation around the social acceptability of CTAs is. In this paper, we investigate these aspects by surveying 80 participants (40 from Australia, 40 from France). Our study reveals interesting results on CTA usage, experiences, and user perceptions. We found that privacy concerns, tech unawareness, app requisites, and mistrust can reduce the users’ willingness to use CTAs. We conclude by presenting ways to foster public trust and meet users’ privacy expectations that in turn support CTA’s adoption.

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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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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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