arXiv 2202.013482022-02-03cs.CR

VindiCo：人在环IoT中基于自适应的隐私保护

VindiCo: Privacy Safeguard Against Adaptation Based Spyware in Human-in-the-Loop IoT

Salma Elmalaki, Bo-Jhang Ho, Moustafa Alzantot, Yasser Shoukry, Mani Srivastava

备注：: 21 pages. First part of this work is published in the SafeThings'19: IEEE Security and Privacy Workshops (S&P Workshop) and in CitiFog'18: Proceedings of the 1st ACM International Workshop on Smart Cities and Fog Computing (Sensys'18 Workshop)
发表信息：: 2019 IEEE Security and Privacy Workshops (SPW), CitiFog'18: Proceedings of the 1st ACM International Workshop on Smart Cities and Fog Computing
DOI：: 10.1109/SPW.2019.00039 10.1145/3277893.3277901

摘要：

个性化物联网根据上下文信息（如用户行为和位置）调整其行为。不幸的是，个性化物联网适应用户环境的事实打开了一个泄露用户私人信息的侧通道。为此，我们首先研究恶意窃听者可以在多大程度上监控物联网系统所采取的行动并提取用户的私人信息。特别是，我们展示了两个具体的实例（在手机和灵巧家庭的上下文中）的一个新的间谍软件类别，我们称之为上下文感知的适应基于间谍软件（SpyCon）。实验结果表明，SpyCon能够准确预测用户的日常行为，预测准确率达到90.3%.本文的其余部分致力于介绍VindiCo，这是一种旨在检测和缓解可能的SpyCon的软件机制。作为没有已知先前签名或行为的新间谍软件，基于代码签名或应用程序行为的传统间谍软件检测不足以检测SpyCon。因此，VindiCo提出了一种新的基于信息的检测引擎以及几种缓解技术，以抑制检测到的SpyCon提取私人信息的能力。通过具有通用的检测和缓解引擎，VindiCo对SpyCon使用的推理算法是不可知的。我们的研究结果表明，VindiCo减少SpyCon的能力，推断用户上下文从90.3%的基准精度（精度基于随机猜测），可以忽略不计的执行开销。

英文摘要：

Personalized IoT adapts their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapts to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions taken by an IoT system and extract users' private information. In particular, we show two concrete instantiations (in the context of mobile phones and smart homes) of a new category of spyware which we refer to as Context-Aware Adaptation Based Spyware (SpyCon). Experimental evaluations show that the developed SpyCon can predict users' daily behavior with an accuracy of 90.3%. The rest of this paper is devoted to introducing VindiCo, a software mechanism designed to detect and mitigate possible SpyCon. Being new spyware with no known prior signature or behavior, traditional spyware detection that is based on code signature or app behavior is not adequate to detect SpyCon. Therefore, VindiCo proposes a novel information-based detection engine along with several mitigation techniques to restrain the ability of the detected SpyCon to extract private information. By having general detection and mitigation engines, VindiCo is agnostic to the inference algorithm used by SpyCon. Our results show that VindiCo reduces the ability of SpyCon to infer user context from 90.3% to the baseline accuracy (accuracy based on random guesses) with negligible execution overhead.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。