基于位置服务中的轨迹和策略感知的可重用性
Trajectory and Policy Aware Sender Anonymity in Location Based Services
摘要：
我们考虑基于位置的服务（LBS）设置，其中LBS提供商记录移动终端用户在一段时间内发送的请求，并且稍后想要发布/共享这些日志。日志共享对于广告、数据采掘研究和网络管理来说可能是极其有价值的，但它对LBS用户的隐私构成了严重威胁。匿名解决方案防止恶意攻击者通过在获得对匿名日志的访问之后将LBS用户与他们的服务请求相关联来推断LBS用户的兴趣。随着智能手机的快速普及以及对历史用户轨迹变得越来越容易访问的担忧，任何发送方匿名解决方案都有必要保护免受攻击者的攻击，这些攻击者是匿名感知的（即可以访问历史用户轨迹）以及策略感知的（即他们知道日志匿名化策略）。我们称这种攻击者为TP感知的。本文介绍了第一个隐私保证对TP感知攻击者，称为TP感知发送者k-匿名。事实证明，对于相同的LBS日志，存在许多可能的TP感知匿名化，每种匿名化日志对匿名化日志的消费者具有不同的效用。研究了如何找到最优的TP感知匿名化算法。我们发现，容错意识使问题在计算上比在文献中发现的容错意识的变体（NP-完全的日志的大小，与PTIME）更难。我们描述了一个PTIME l-近似算法的轨迹长度为l和经验表明，它的规模大LBS日志（高达200万用户）。
英文摘要：
We consider Location-based Service (LBS) settings, where a LBS provider logs the requests sent by mobile device users over a period of time and later wants to publish/share these logs. Log sharing can be extremely valuable for advertising, data mining research and network management, but it poses a serious threat to the privacy of LBS users. Sender anonymity solutions prevent a malicious attacker from inferring the interests of LBS users by associating them with their service requests after gaining access to the anonymized logs. With the fast-increasing adoption of smartphones and the concern that historic user trajectories are becoming more accessible, it becomes necessary for any sender anonymity solution to protect against attackers that are trajectory-aware (i.e. have access to historic user trajectories) as well as policy-aware (i.e they know the log anonymization policy). We call such attackers TP-aware. This paper introduces a first privacy guarantee against TP-aware attackers, called TP-aware sender k-anonymity. It turns out that there are many possible TP-aware anonymizations for the same LBS log, each with a different utility to the consumer of the anonymized log. The problem of finding the optimal TP-aware anonymization is investigated. We show that trajectory-awareness renders the problem computationally harder than the trajectory-unaware variants found in the literature (NP-complete in the size of the log, versus PTIME). We describe a PTIME l-approximation algorithm for trajectories of length l and empirically show that it scales to large LBS logs (up to 2 million users).
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。
