Fast or Secure? Push the Limit of Privacy Leakage Threat via Charging Side-Channel Attacks
Jiaxin Jiang, Xutong Zhang, Jiahao Li, Leqi Zhao, Zhengxin Guo, Kaiyan Cui, Ming Gao, Jinsong Han, Fu Xiao
摘要
Mobile devices have become ubiquitous, along with charging technology evolving rapidly. However, recent research has revealed a privacy vulnerability. Power traces in USB cables during charging can be maliciously exploited to eavesdrop on users' private information. Existing attacks typically measure either voltage or current to characterize power traces in low-power USB charging (5V 1A). In this paper, we present a novel charging side-channel attack that broadens the attack surface to include fast charging. Our approach works for both wired and wireless charging scenarios. It combines both voltage and current traces for a more comprehensive threat analysis. We systematically explore the risk of information leakage during different operations, including screen inputs (password and keystroke inference), screen outputs (application/display detection), acoustic outputs (loudspeaker eavesdropping), and acoustic inputs (microphone eavesdropping). In particular, such duplex acoustic eavesdropping via charging side channels has not been studied before. Extensive experiments demonstrate that our attacks pose real-world threats. As a countermeasure, we propose design suggestions for future fast charging systems to prevent privacy leaks.
• Security and privacy → Side-channel analysis and countermeasures; Mobile and wireless security.
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它引用的顶会 Paper12
- Speechless: Analyzing the Threat to Speech Privacy from Smartphone Motion SensorsS. Abhishek Anand, Nitesh SaxenaS&P 2018 · 被引用 110 次
- Charger-Surfing: Exploiting a Power Line Side-Channel for Smartphone Information LeakagePatrick Cronin, Xing Gao, Chengmo Yang, Haining WangUSENIX Security 2021 · 被引用 62 次
- Wireless Charging Power Side-Channel AttacksAlexander S. La Cour, Khurram K. Afridi, G. Edward SuhCCS 2021 · 被引用 40 次
- Exploiting Contactless Side Channels in Wireless Charging Power Banks for User Privacy Inference via Few-shot LearningTao Ni, Jianfeng Li, Xiaokuan Zhang, Chaoshun Zuo 等MobiCom 2023 · 被引用 27 次
- InertiEAR: Automatic and Device-independent IMU-based Eavesdropping on SmartphonesMing Gao, Yajie Liu, Yike Chen, Yimin Li 等INFOCOM 2022 · 被引用 18 次
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