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首页> 外文期刊>IEEE Transactions on Information Theory >Matching Anonymized and Obfuscated Time Series to Users’ Profiles
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Matching Anonymized and Obfuscated Time Series to Users’ Profiles

机译：将匿名和混淆时间序列与用户个人资料匹配

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摘要

Many popular applications use traces of user data to offer various services to their users. However, even if user data are anonymized and obfuscated, a user's privacy can be compromised through the use of statistical matching techniques that match a user trace to prior user behavior. In this paper, we derive the theoretical bounds on the privacy of users in such a scenario. We build on our recent study in the area of location privacy, in which we introduced formal notions of location privacy for anonymization-based location privacy-protection mechanisms. Here, we derive the fundamental limits of user privacy when both anonymization and obfuscation-based protection mechanisms are applied to users' time series of data. We investigate the impact of such mechanisms on the tradeoff between privacy protection and user utility. We first study achievability results for the case where the time-series of users are governed by an independent and identically distributed (i.i.d.) process. The converse results are proved both for the i.i.d. case as well as the more general Markov chain model. We demonstrate that as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect privacy; and, in the second region, no user has privacy.
机译：许多流行的应用程序使用用户数据的痕迹为用户提供各种服务。但是，即使对用户数据进行匿名处理和混淆处理，也可以通过使用将用户跟踪与先前用户行为进行匹配的统计匹配技术来损害用户的隐私。在本文中，我们推导了在这种情况下用户隐私的理论界限。我们以最近在位置隐私领域的研究为基础，在该研究中，我们为基于匿名化的位置隐私保护机制引入了位置隐私的正式概念。在此，当匿名化和基于混淆的保护机制都应用于用户的数据时间序列时，我们得出了用户隐私的基本限制。我们研究了这种机制对隐私保护和用户实用程序之间权衡的影响。我们首先研究针对用户的时间序列受独立且均匀分布（i.i.d.）流程控制的情况的可实现性结果。在i.i.d中证明了相反的结果。案例以及更一般的马尔可夫链模型。我们证明，随着网络中用户数量的增长，混淆匿名平面可以分为两个区域：在第一个区域中，所有用户都拥有完美的隐私；在第二区域，没有用户拥有隐私。

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