A differentially private algorithm for location data release
A differentially private algorithm for location data release
复制标题
DOI：
10.1007/s10115-015-0856-1
复制
发表时间：
2015-07
影响因子：
2.7
通讯作者：
P. Xiong;Tianqing Zhu;Wenjia Niu;Gang Li
中科院分区：
文献类型：
--
作者：
P. Xiong;Tianqing Zhu;Wenjia Niu;Gang Li
近年来，移动的技术的兴起导致了大量的位置信息，这些位置信息是用于诸如旅行模式挖掘和交通分析的知识发现的宝贵资源。然而，位置数据集已经面临着严重的隐私问题，因为攻击者可以重新识别用户和他/她的敏感信息，从这些数据集只有很少的背景知识。近年来，一些隐私保护技术被提出来解决这个问题，但大多数都缺乏严格的隐私概念，很难抵御可能的攻击的数量。本文提出了一种基于严格隐私概念的位置数据随机化算法，即差分隐私算法，以保护用户身份和敏感信息为目标。该算法旨在掩盖每个用户的确切位置以及用户访问具有给定隐私预算的位置的频率。它包括三个隐私保护操作：私有位置聚类缩小随机域和聚类权重扰动隐藏位置的权重，而私有位置选择隐藏用户的确切位置。对隐私和效用的理论分析证实了所发布的位置数据的隐私和效用之间的改进的权衡。在GeoLife、Flickr、Div 400和Instagram这四个真实的社交网站上进行了大量的实验。实验结果进一步表明，这种私有发布算法可以成功地保留数据集的实用性，同时保护用户的隐私。
The rise of mobile technologies in recent years has led to large volumes of location information, which are valuable resources for knowledge discovery such as travel patterns mining and traffic analysis. However, location dataset has been confronted with serious privacy concerns because adversaries may re-identify a user and his/her sensitivity information from these datasets with only a little background knowledge. Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion,differential privacy, with the goal of preserving users’ identities and sensitive information. The algorithm aims to mask the exact locations of each user as well as the frequency that the user visits the locations with a given privacy budget. It includes three privacy-preserving operations:private location clusteringshrinks the randomized domain andcluster weight perturbationhides the weights of locations, whileprivate location selectionhides the exact locations of a user. Theoretical analysis on privacy and utility confirms an improved trade-off between privacy and utility of released location data. Extensive experiments have been carried out on four real-world datasets,GeoLife,Flickr,Div400andInstagram. The experimental results further suggest that this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.