CAP: A Context-Aware Privacy Protection System for Location-Based Services

Pingley, Aniket; Yu, Wei; Zhang, Nan; Fu, Xinwen; Zhao, Wei

doi:10.1109/icdcs.2009.62

Cited by 81 publications

(50 citation statements)

References 31 publications

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“…To avoid the use of TTP, many mobile device-based schemes [4,6,9,10,14,15,[29][30][31][32][33] are introduced into LBS privacy protection LBS system. 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.…”

Section: Location Privacy Protectionmentioning

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%

“…Furthermore, it incurs the high computation overhead at client-side. Mobile device-based approaches remove the requirement of a TTP by using k-anonymity [10,15], location obfuscation and perturbation [4,6,9], and private information retrieval (PIR) [14]. However, PIR may incur high computation and communication costs unaffordable to mobile devices and LBS server.…”

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%

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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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Section: Location Privacy Protectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 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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Semantic analysis in location privacy preserving

Xiong

Zhang

Zhu

2015

Concurrency and Computation

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SUMMARYWith the increasing use of location-based services, location privacy has recently started raising serious concerns. Location perturbation and obfuscation are most widely used for location privacy preserving. To protect a user from being identified, a cloaked spatial region that contains other k 1 nearest neighbors of the user is submitted to the location-based service provider, instead of the accurate position. In this paper, we consider the location-aware applications that services are different among regions. In such scenarios, the semantic distance between users should be considered besides the Euclidean distance for searching the neighbors of a user. We define a novel distance measurement that combines the semantic and the Euclidean distance to address the privacy-preserving issue in the aforementioned applications. We also present an algorithm kNNH to implement our proposed method. Moreover, we conduct performance study experiments on the proposed algorithm. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users' privacy. Copyright

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A Stochastic Preserving Scheme of Location Privacy

Iyengar

Boroojeni

2014

Mathematical Theories of Distributed Sensor Networks

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CAP: A Context-Aware Privacy Protection System for Location-Based Services

Cited by 81 publications

References 31 publications

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

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

Semantic analysis in location privacy preserving

A Stochastic Preserving Scheme of Location Privacy

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