Christopher Ellis (The Ohio State University), Yue Zhang (Drexel University), Mohit Kumar Jangid (The Ohio State University), Shixuan Zhao (The Ohio State University), Zhiqiang Lin (The Ohio State University)

Wireless technologies like Bluetooth Low Energy (BLE) and Wi-Fi are essential to the Internet of Things (IoT), facilitating seamless device communication without physical connections. However, this convenience comes at a cost—exposed data exchanges that are susceptible to observation by attackers, leading to serious security and privacy threats such as device tracking. Although protocol designers have traditionally relied on strategies like address and identity randomization as a countermeasure, our research reveals that these attacks remain a significant threat due to a historically overlooked, fundamental flaw in exclusive-use wireless communication. We define _exclusive-use_ as a scenario where devices are designed to provide functionality solely to an
associated or paired device. The unique communication patterns inherent in these relationships create an observable boolean side-channel that attackers can exploit to discover whether two devices “trust” each other. This information leak allows for the deanonymization of devices, enabling tracking even in the presence of modern countermeasures. We introduce our tracking attacks as _IDBleed_ and demonstrate that BLE and Wi-Fi protocols that support confidentiality, integrity, and authentication remain vulnerable to deanonymization due to this fundamental flaw in exclusive-use communication patterns. Finally, we propose and quantitatively evaluate a generalized, privacy-preserving mitigation we call _Anonymization Layer_ to find a negligible 2% approximate overhead in performance and power consumption on tested smartphones and PCs.

View More Papers

Provably Unlearnable Data Examples

Derui Wang (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Bo Li (The University of Chicago), Seyit Camtepe (CSIRO's Data61), Liming Zhu (CSIRO's Data61)

Read More

YuraScanner: Leveraging LLMs for Task-driven Web App Scanning

Aleksei Stafeev (CISPA Helmholtz Center for Information Security), Tim Recktenwald (CISPA Helmholtz Center for Information Security), Gianluca De Stefano (CISPA Helmholtz Center for Information Security), Soheil Khodayari (CISPA Helmholtz Center for Information Security), Giancarlo Pellegrino (CISPA Helmholtz Center for Information Security)

Read More

Characterizing the Impact of Audio Deepfakes in the Presence...

Magdalena Pasternak (University of Florida), Kevin Warren (University of Florida), Daniel Olszewski (University of Florida), Susan Nittrouer (University of Florida), Patrick Traynor (University of Florida), Kevin Butler (University of Florida)

Read More

Evaluating LLMs Towards Automated Assessment of Privacy Policy Understandability

Keika Mori (Deloitte Tohmatsu Cyber LLC, Waseda University), Daiki Ito (Deloitte Tohmatsu Cyber LLC), Takumi Fukunaga (Deloitte Tohmatsu Cyber LLC), Takuya Watanabe (Deloitte Tohmatsu Cyber LLC), Yuta Takata (Deloitte Tohmatsu Cyber LLC), Masaki Kamizono (Deloitte Tohmatsu Cyber LLC), Tatsuya Mori (Waseda University, NICT, RIKEN AIP)

Read More