Video Aficionado: We Know What You Are Watching
Video Aficionado: We Know What You Are Watching
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DOI：
10.1109/tmc.2020.3045730
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发表时间：
2020-12
影响因子：
7.9
通讯作者：
Jialing He;Zijian Zhang;Jian Mao;Liran Ma;B. Khoussainov;Rui Jin;Liehuang Zhu
中科院分区：
文献类型：
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作者：
Jialing He;Zijian Zhang;Jian Mao;Liran Ma;B. Khoussainov;Rui Jin;Liehuang Zhu
用户享受在智能设备上观看视频的便利。然而，视频观看记录可能会在用户不知情的情况下被泄露，并被利用来推断私人信息。在本文中，我们设计并实现了一种新的侧信道攻击系统，名为video aficionado，它可以在不违反Android上任何访问控制策略的情况下识别视频观看信息。我们的系统只需要收集视频播放应用程序的功耗数据，不需要明确的用户许可。收集到的数据被发送到远程服务器，其中噪声由经过多层感知器 (MLP) 训练的分类器进行清除和识别。我们通过一系列精心设计的实验来评估我们提出的系统。实验结果表明，我们的系统可以对从 20 个视频中收集的 3918 个功率测量片段中的每个 20 秒功率测量片段进行平均 74.5% 的识别准确率。据我们所知，视频爱好者是智能设备上第一个基于实时功耗的视频识别系统。
Users enjoy the convenience of watching videos on smart devices. However, video watching records can be exposed without users’ knowledge and be exploited to infer private information. In this paper, we design and implement a new side-channel attack system, named video aficionado, which can identify video watching information without violating any access control policies on Android. Our system only needs to collect power consumption data of a video playing app, which does not require explicit user permission. The collected data is sent to a remote server, where noise is cleaned and identified by a multi-layer perceptron (MLP) trained classifier. We evaluate our proposed system through a set of carefully designed experiments. Experimental results demonstrate that our system can make an identification with 74.5 percent accuracy on average for each 20-second power measurement segment out of 3918 segments collected from 20 videos. To the best of our knowledge, video aficionado is the first real-time power consumption-based video identification system on smart devices.