Privacy vulnerability of published anonymous mobility traces

Y.T., Chris; Yau, David K. Y.; Yip, Nung Kwan; Rao, Nageswara S. V.

doi:10.1145/1859995.1860017

Cited by 84 publications

(72 citation statements)

References 16 publications

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“…Second, although dummy locations can be used to achieve k-anonymity, how to select these locations is a challenge. Most of the existing approaches [6], [7], [8], [4], [9] assume that the adversary has no side information [10], [11], such as user's query probability related to location and time, and information related to the semantics of the query such as the gender and social status of the user, and then dummy locations are generated based on a random walk model [7], [4], or virtual circle/grid model [9]. Since some adversary (e.g., the LBS server) may have such side information, these dummy generation algorithms may not work well.…”

Section: Introductionmentioning

confidence: 99%

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

Niu

Zhu

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

369

357

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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.

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Section: Introductionmentioning

confidence: 99%

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

Niu

Zhu

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

369

357

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“…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.…”

Section: Modified Hilbert Curve Fillingmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…However, PIR may incur high computation and communication costs unaffordable to mobile devices and LBS server. The k-anonymity [7,10,15] assumes that the adversary has no side information about the user [11,12], such as approximate location, mobility profile, query frequency, and user profiles. In reality, since some adversary (e.g., the LSP) may possess such side information, these methods are inadequate to protect the user's location privacy [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhang

Huang

et al. 2018

Wireless Communications and Mobile Computing

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The proliferation of location-based services, representative services for the mobile networks, has posed a serious threat to users' privacy. In the literature, several privacy mechanisms have been proposed to preserve location privacy. Location obfuscation enforced using cloaking region is a widely used technique to achieve location privacy. However, it requires a trusted third-party (TTP) and cannot sufficiently resist various inference attacks based on background information and thus is vulnerable to location privacy breach. In this paper, we propose a context-aware location privacy-preserving solution with differential perturbations, which can enhance the user's location privacy without requiring a TTP. Our scheme utilizes the modified Hilbert curve to project every 2-d location of the user in the considered map to 1-d space and randomly generates the reasonable perturbation by adding Laplace noise via differential privacy. In order to solve the resource limitation of mobile devices, we use a quad-tree based scheme to transform and store the user context information as bit stream which achieves the high compression ratio and supports efficient retrieval. Security analysis shows that our proposed scheme can effectively preserve the location privacy. Experimental evaluation shows that our scheme retrieval accuracy is increased by an average of 15.4% compared with the scheme using standard Hilbert curve. Our scheme can provide strong privacy guarantees with a bounded accuracy loss while improving retrieval accuracy.

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Introduction

Liu¹,

Li²

2013

Location Privacy Protection in Mobile Networks

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Privacy vulnerability of published anonymous mobility traces

Cited by 84 publications

References 16 publications

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

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

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

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