Achieving k-anonymity in privacy-aware location-based services

Abstract

Abstract-Location-Based Service (LBS) has become a vital part of our daily life. While enjoying the convenience provided by LBS, users may lose privacy since the untrusted LBS server has all the information about users in LBS and it may track them in various ways or release their personal data to third parties. To address the privacy issue, we propose a DummyLocation Selection (DLS) algorithm to achieve k-anonymity for users in LBS. Different from existing approaches, the DLS algorithm carefully selects dummy locations considering that side information may be exploited by adversaries. We first choose these dummy locations based on the entropy metric, and then propose an enhanced-DLS algorithm, to make sure that the selected dummy locations are spread as far as possible. Evaluation results show that the proposed DLS algorithm can significantly improve the privacy level in terms of entropy. The enhanced-DLS algorithm can enlarge the cloaking region while keeping similar privacy level as the DLS algorithm.

A Context‐Aware Location Differential Perturbation Scheme for Privacy‐Aware Users in Mobile Environment

“…Location k-anonymity [3] and its variation like l-diversity [20] and t-closeness [21] are proposed to measure the ability of the adversary to differentiate the real user from others within the anonymity set. To overcome the drawbacks of k-anonymity in quantifying location privacy, entropy-based metrics have been adopted in [5,13,22,23] for quantifying the information an adversary can obtain from one (or a series) of location update(s). Nonetheless, Shokri et al [24] show a lack of satisfactory correlation between these two metrics and the success of the adversary in inferring the users' actual position.…”

“…For location obfuscation mechanisms, most of them employ well-known location k-anonymity to protect user's privacy by blurring user's exact location into a sufficiently larger CR. Because of its simplicity, k-anonymity metric has been widely adopted in many different methods, including IntervalCloak [3], clique-based cloak [5], location differential perturbations [8], game-theoretic approach [12], dummy location selection [13], and hilbASR [28]. However, these methods suffer from the single point of failure due to the reliance on a TTP named anonymizer.…”

“…However, k-anonymity based schemes [10,13,[29][30][31][32][33] still need to generate CR via exchanged information from other encountered mobile users. Thus, they also cannot resist homogeneity attacks and background knowledge attacks.…”

“…To address the privacy issues for mobile users in LBSs, a variety of privacy-preserving mechanisms and metrics have been proposed to allow users to make use of the LBSs while mitigating privacy concerns over the past few years [3][4][5][6][7][8][9][10][11][12][13][14][15]. These LBS privacy protection mechanisms (LPPMs) provide different privacy-utility trade-off, which offer alternatives to better meet personal requirements of different mobile users.…”

“…These LBS privacy protection mechanisms (LPPMs) provide different privacy-utility trade-off, which offer alternatives to better meet personal requirements of different mobile users. Roughly speaking, these LPPMs can be divided into two categories according to their architecture [16]: trusted anonymization server-based schemes [3,5,7,8,11,13] and mobile devices-based schemes [4,6,9,10,14,15]. In trusted anonymization serve-based schemes, a trusted thirdparty server (e.g., anonymizer [3]) is employed to perturb, obfuscate, and cloak user's query location by using the notion of k-anonymity [3].…”