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Map-Aware Position Sharing for Location Privacy in Non-trusted Systems

机译:非信任系统中的地图感知位置共享,用于位置隐私

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Many current location-based applications (LBA) such as friend finder services use information about the positions of mobile users. So-called location services (LSs) have been proposed to manage these mobile user positions efficiently. However, managing user positions raises privacy issues, in particular, if the providers of LSs are only partially trusted. Therefore, we presented the concept of private position sharing for partially trusted systems in a previous paper [1]. The basic idea of position sharing is to split the precise user position into a set of position shares of well-defined limited precision and distribute these shares among LSs of different providers. The main contributions of this paper are two extended position sharing approaches that improve our previous approach in two ways: Firstly, we reduce the predictability of share generation that allows an attacker to gain further information from a sub-set of shares to further increase the position precision. Secondly, we present a position sharing algorithm for constrained movement scenarios whereas the existing approach was tailored to open space environments. However, open space approaches are vulnerable to map-based attacks. Therefore, we present a share generation algorithm that takes map knowledge into account.
机译:当前许多基于位置的应用程序(LBA),例如朋友查找器服务,都使用有关移动用户位置的信息。已经提出了所谓的位置服务(LS)来有效地管理这些移动用户位置。但是,管理用户位置会引起隐私问题,尤其是在仅部分信任LS的提供者的情况下。因此,我们在先前的论文[1]中提出了针对部分受信任系统的私人职位共享的概念。位置共享的基本思想是将精确的用户位置划分为一组定义明确的有限精度的位置份额,并在不同提供商的LS之间分配这些份额。本文的主要贡献是两种扩展的头寸共享方法,它们以两种方式改进了我们以前的方法:首先,我们降低了股票生成的可预测性,使攻击者可以从子股票集中获取更多信息,从而进一步增加头寸精确。其次,我们提出了一种针对受限运动场景的位置共享算法,而现有方法是针对开放空间环境量身定制的。但是,开放空间方法容易受到基于地图的攻击。因此,我们提出了一种将地图知识考虑在内的份额生成算法。

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