aLeak: Privacy Leakage through Context - Free Wearable Side-Channel
aLeak: Privacy Leakage through Context - Free Wearable Side-Channel
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DOI：
10.1109/infocom.2018.8485958
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Yang Liu;Zhenjiang Li
中科院分区：
文献类型：
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作者：
Yang Liu;Zhenjiang Li
本文回顾了一个关键的隐私问题--用户在移动设备上频繁输入的密码和个人数据等敏感信息是否可以通过用户手腕上的可穿戴设备(如智能手表或腕带)的运动传感器来推断？现有的工作已经在某些上下文感知条件下实现了初步的成功，例如1)水平小键盘平面，2)已知的键盘大小，3)和/或在固定的“Enter”按钮上的最后一次击键。更进一步，本文的关键贡献是充分展示，更重要的是警告人们，在更普遍的上下文无关场景中输入隐私泄露的进一步风险，这些场景与我们大多数人日常使用移动设备有关。我们通过解决一系列悬而未决的挑战和开发一个原型系统aLeak来验证这一可行性。广泛的实验表明了aLeak的有效性，它在没有任何上下文相关信息的情况下，从各种移动平台上300多轮不同用户的输入中获得了令人满意的攻击成功率。
We revisit a crucial privacy problem in this paper - can the sensitive information, like the passwords and personal data, frequently typed by user on mobile devices be inferred through the motion sensors of wearable device on user's wrist, e.g., smart watch or wrist band? Existing works have achieved the initial success under certain context-aware conditions, such as 1) the horizontal keypad plane, 2) the known keyboard size, 3) and/or the last keystroke on a fixed “enter” button. Taking one step further, the key contribution of this paper is to fully demonstrate, more importantly alarm people, the further risks of typing privacy leakage in much more generalized context-free scenarios, which are related to most of us for the daily usage of mobile devices. We validate this feasibility by addressing a series of unsolved challenges and developing a prototype system aLeak. Extensive experiments show the efficacy of aLeak, which achieves promising successful rates in the attack from more than 300 rounds of different users' typings on various mobile platforms without any context-related information.