Hetvi Shastri (University of Massachusetts Amherst), Akanksha Atrey (Nokia Bell Labs), Andre Beck (Nokia Bell Labs), Nirupama Ravi (Nokia Bell Labs)

The recent emergence of decentralized wireless networks empowers individual entities to own, operate, and offer subscriptionless connectivity services in exchange for monetary compensation. While traditional connectivity providers have built trust over decades through widespread adoption, established practices, and regulation, entities in a decentralized wireless network, lacking this foundation, may be incentivized to exploit the service for their own advantage. For example, a dishonest hotspot operator can intentionally violate the agreed upon connection terms in an attempt to increase their profits. In this paper, we examine and develop a taxonomy of adversarial behavior patterns in decentralized wireless networks. Our case study finds that provider-driven attacks can potentially more than triple provider earnings. We conclude the paper with a discussion on the critical need to develop novel techniques to detect and mitigate adversarial behavior in decentralized wireless networks.

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URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

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Black-box Membership Inference Attacks against Fine-tuned Diffusion Models

Yan Pang (University of Virginia), Tianhao Wang (University of Virginia)

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Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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Transparency or Information Overload? Evaluating Users’ Comprehension and Perceptions...

Xiaoyuan Wu (Carnegie Mellon University), Lydia Hu (Carnegie Mellon University), Eric Zeng (Carnegie Mellon University), Hana Habib (Carnegie Mellon University), Lujo Bauer (Carnegie Mellon University)

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