Smartphone speech privacy concerns from side-channel attacks on facial biomechanics

被引:5
|
作者
Griswold-Steiner, Isaac [1 ]
LeFevre, Zachary [1 ]
Serwadda, Abdul [1 ]
机构
[1] Texas Tech Univ, Dept Comp Sci, Lubbock, TX 79409 USA
基金
美国国家科学基金会;
关键词
Smartphone privacy; Motion sensors; Side-channel attack; User identification; Speech privacy;
D O I
10.1016/j.cose.2020.102110
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Speech is a complex orchestration of physical movements which involves the lungs, vocal cords, face, jaw, and mouth. As we speak on the phone, we inadvertently impart energy on the mobile device at our ear, causing it to move as our face shapes words and sounds. We theorize that different phonetics from the International Phonetic Alphabet (IPA), which act as the building blocks of speech, may have their own fingerprint on motion sensor data during a phone conversation. When phonetics are combined into words, the relationship between phonetics and motion sensor data could cause words to also be identifiable. Based on an initial investigation into the relationship between phonetics and motion sensor data, we develop attacks to evaluate the risk that this could pose to user privacy. We evaluate attacks for classifying digits, differentiating between digit and non-digit speech, identifying the gender of the user, and user identification. The results of these experiments in various configurations demonstrate that the attacks can be highly effective. Our research adds to the body of work making the case for additional measures to control and protect data produced by users and their devices. Without action on the part of technology producers, users will remain vulnerable to attacks which leverage APIs that leave the user without any ability to control the data that their devices generate. (C) 2020 Published by Elsevier Ltd.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Enhanced Side-Channel Cube Attacks on PRESENT
    Zhao, Xinjie
    Guo, Shize
    Zhang, Fan
    Wang, Tao
    Shi, Zhijie
    Luo, Hao
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2013, E96A (01) : 332 - 339
  • [42] Side-Channel Attacks on Fingerprint Matching Algorithms
    Duermuth, Markus
    Oswald, David
    Pastewka, Niklas
    TRUSTED'16: PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON TRUSTWORTHY EMBEDDED DEVICES, 2016, : 3 - 13
  • [43] Beyond the CPU: Side-Channel Attacks on GPUs
    Naghibijouybari, Hoda
    Neupane, Ajaya
    Qian, Zhiyun
    Abu-Ghazaleh, Nael
    IEEE DESIGN & TEST, 2021, 38 (03) : 15 - 21
  • [44] On the Challenges of Detecting Side-Channel Attacks in SGX
    Jiang, Jianyu
    Soriente, Claudio
    Karame, Ghassan
    PROCEEDINGS OF 25TH INTERNATIONAL SYMPOSIUM ON RESEARCH IN ATTACKS, INTRUSIONS AND DEFENSES, RAID 2022, 2022, : 86 - 98
  • [45] SonarSnoop: active acoustic side-channel attacks
    Peng Cheng
    Ibrahim Ethem Bagci
    Utz Roedig
    Jeff Yan
    International Journal of Information Security, 2020, 19 : 213 - 228
  • [46] Acoustic Side-Channel Attacks on a Computer Mouse
    Conti, Mauro
    Duroyon, Marin
    Orazi, Gabriele
    Tsudik, Gene
    DETECTION OF INTRUSIONS AND MALWARE, AND VULNERABILITY ASSESSMENT, DIMVA 2024, 2024, 14828 : 44 - 63
  • [47] Cache Side-Channel Attacks in Cloud Computing
    Younis, Younis
    Kifayat, Kashif
    Merabti, Madjid
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON CLOUD SECURITY MANAGEMENT (ICCSM-2014), 2014, : 138 - 146
  • [48] Remote Side-Channel Attacks on Heterogeneous SoC
    Gravellier, Joseph
    Dutertre, Jean-Max
    Teglia, Yannick
    Moundi, Philippe Loubet
    Olivier, Francis
    SMART CARD RESEARCH AND ADVANCED APPLICATIONS, CARDIS 2019, 2020, 11833 : 109 - 125
  • [49] Side-Channel Attacks on Optane Persistent Memory
    Liu, Sihang
    Kanniwadi, Suraaj
    Schwarzl, Martin
    Kogler, Andreas
    Gruss, Daniel
    Khan, Samira
    PROCEEDINGS OF THE 32ND USENIX SECURITY SYMPOSIUM, 2023, : 6807 - 6824
  • [50] Side-Channel Attacks Based on Collaborative Learning
    Liu, Biao
    Ding, Zhao
    Pan, Yang
    Li, Jiali
    Feng, Huamin
    DATA SCIENCE, PT 1, 2017, 727 : 549 - 557