Youngwook Do (JPMorganChase and Georgia Institute of Technology), Tingyu Cheng (Georgia Institute of Technology and University of Notre Dame), Yuxi Wu (Georgia Institute of Technology and Northeastern University), HyunJoo Oh(Georgia Institute of Technology), Daniel J. Wilson (Northeastern University), Gregory D. Abowd (Northeastern University), Sauvik Das (Carnegie Mellon University)

Passive RFID is ubiquitous for key use-cases that include authentication, contactless payment, and location tracking. Yet, RFID chips can be read without users’ knowledge and consent, causing security and privacy concerns that reduce trust. To improve trust, we employed physically-intuitive design principles to create On-demand RFID (ORFID). ORFID’s antenna, disconnected by default, can only be re-connected by a user pressing and holding the tag. When the user lets go, the antenna automatically disconnects. ORFID helps users visibly examine the antenna’s connection: by pressing a liquid well, users can observe themselves pushing out a dyed, conductive liquid to fill the void between the antenna’s two bisected ends; by releasing their hold, they can see the liquid recede. A controlled evaluation with 17 participants showed that users trusted ORFID significantly more than a commodity RFID tag, both with and without an RFID-blocking wallet. Users attributed this increased trust to visible state inspection and intentional activation.

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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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A Large-Scale Measurement Study of the PROXY Protocol and...

Stijn Pletinckx (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara)

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MingledPie: A Cluster Mingling Approach for Mitigating Preference Profiling...

Cheng Zhang (Hunan University), Yang Xu (Hunan University), Jianghao Tan (Hunan University), Jiajie An (Hunan University), Wenqiang Jin (Hunan University)

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