Protecting Location Privacy against Location-Dependent Attacks in Mobile Services

Abstract

Privacy preservation has recently received considerable attention for location-based mobile services. In this paper, we present location-dependent attack resulting from continuous and dependent location updates and propose an incremental clique-based cloaking algorithm, called ICliqueCloak, to defend against location-dependent attack. The main idea is to incrementally maintain maximal cliques for location cloaking in an un-directed graph that takes into consideration the effect of continuous location updates.

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.…”

“…To assign a corresponding range in the 1-d space for each base cell, we Input: the T obtained from Algorithm 1, starting point 0 and curve orientation Output: a updated quad-tree root node T (1) initializes S(T) = 0 , (T)= , m = 0; (2) push T into the stack; (3) while (stack is not empty) do (4) N = pop the top element from the stack (5) if (N has child node) then (6) for (i= sw, se, ne, nw) do (7) set S(N i ), and (N i ) (8) push N i into the stack (9) end for (10) else (11) ℎ N = m (12) set the values of all corresponding POIs in the node N as m (13) m = m + 1 (14) end if (15) end while (16) outputs the updated quad-tree root node T.…”

“…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].…”