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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LLMPirate: LLMs for Black-box Hardware IP Piracy

Vasudev Gohil (Texas A&M University), Matthew DeLorenzo (Texas A&M University), Veera Vishwa Achuta Sai Venkat Nallam (Texas A&M University), Joey See (Texas A&M University), Jeyavijayan Rajendran (Texas A&M University)

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Automatic Library Fuzzing through API Relation Evolvement

Jiayi Lin (The University of Hong Kong), Qingyu Zhang (The University of Hong Kong), Junzhe Li (The University of Hong Kong), Chenxin Sun (The University of Hong Kong), Hao Zhou (The Hong Kong Polytechnic University), Changhua Luo (The University of Hong Kong), Chenxiong Qian (The University of Hong Kong)

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Query Privacy in Data Spaces

Shuwen Liu (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China), George C. Polyzos (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China and ExcID P.C., Athens, Greece)

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