Collect Responsibly But Deliver Arbitrarily?: A Study on Cross-User Privacy Leakage in Mobile Apps

Shuai Li, Zhemin Yang, Nan Hua, Peng Liu, Xiaohan Zhang, Guangliang Yang, Min Yang
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Abstract

Recent years have witnessed the interesting trend that modern mobile apps perform more and more likely as user-to-user platforms, where app users can be freely and conveniently connected. Upon these platforms, rich and diverse data is often delivered across users, which brings users great conveniences and plentiful services, but also introduces privacy security concerns. While prior work has primarily studied illegitimate personal data collection problems in mobile apps, few paid little attention to the security of this emerging user-to-user platform feature, thus providing a rather limited understanding of the privacy risks in this aspect. In this paper, we focus on the security of the user-to-user platform feature and shed light on its caused insufficiently-studied but critical privacy risk, which is brought forward by cross-user personal data over-delivery (denoted as XPO). For the first time, this paper reveals the landscape of such XPO risk in wild, along with prevalence and severity assessment. To achieve this, we design a novel automated risk detection framework, named XPOChecker, that leverages the advantages of machine learning and program analysis to extensively and precisely identify potential privacy risks during user-to-user connections, and regulate whether the delivered data is legitimate or not. By applying XPOChecker on 13,820 real-world popular Android apps, we find that XPO is prevalent in practice, with 1,902 apps (13.76%) being affected. In addition to the mere exposure of diverse private user data which causes serious and broad privacy infringement, we demonstrate that the XPO exploits can invalidate privacy preservation mechanisms, leak business secrets, and even restore the sensitive membership of victims which potentially poses personal safety threats. Furthermore, we also confirm the existence of XPO risks in iOS apps for the first time. Last, to help understand and prevent XPO, we have responsibly launched two notification campaigns to inform the developers of the affected apps, with the conclusion of five underlying lessons from developers' feedback. We hope our work can make up for the deficiency of the understandings of XPO, help developers avoid XPO, and motivate further researches.
负责收货却随意送货?:移动应用跨用户隐私泄露研究
近年来出现了一个有趣的趋势,即现代移动应用越来越倾向于作为用户对用户的平台,应用用户可以自由便捷地进行连接。在这些平台上,用户之间往往传递着丰富多样的数据,给用户带来了极大的便利和丰富的服务,但也带来了隐私安全问题。虽然之前的工作主要是研究移动应用中的非法个人数据收集问题,但很少有人关注这一新兴的用户对用户平台功能的安全性,因此对这方面的隐私风险的理解相当有限。在本文中,我们将重点关注用户对用户平台特性的安全性,并揭示其引起的研究不足但关键的隐私风险,即跨用户个人数据过度传递(简称XPO)。本文首次揭示了这种XPO风险在野外的概况,以及患病率和严重性评估。为了实现这一目标,我们设计了一种新的自动化风险检测框架,名为XPOChecker,它利用机器学习和程序分析的优势,广泛而精确地识别用户对用户连接过程中的潜在隐私风险,并规范所交付的数据是否合法。通过对13,820个现实世界中流行的Android应用程序应用XPOChecker,我们发现XPO在实践中很普遍,有1,902个应用程序(13.76%)受到影响。除了仅仅暴露各种私人用户数据造成严重和广泛的隐私侵犯外,我们还证明了XPO漏洞可以使隐私保护机制失效,泄露商业秘密,甚至恢复受害者的敏感成员资格,从而潜在地构成人身安全威胁。此外,我们也首次确认iOS应用存在XPO风险。最后,为了帮助理解和预防XPO,我们负责任地发起了两次通知活动,告知开发者受影响的应用,并从开发者的反馈中总结了5个基本教训。我们希望我们的工作可以弥补对XPO认识的不足,帮助开发者避免XPO,并激励进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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