SPIRAL: A Scalable Private Information Retrieval Approach to Location Privacy

Khoshgozaran, Ali; Shirani-Mehr, Houtan; Shahabi, Cyrus

doi:10.1109/mdmw.2008.23

Cited by 35 publications

(16 citation statements)

References 13 publications

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“…The prime shortcoming with such approaches is that different users have different privacy requirement and utility thresholds. Private Information Retrieval (PIR) techniques are also proposed to safeguard the sensitive information like location of the user [23,24]. These solutions have always been very expensive in terms of operations' computation time, communication cost, and resources needed [25].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Location Privacy Solution for Mobile LBS

Gupta

Rao

2017

Mobile Information Systems

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The prevalent usage of location based services, where getting any service is solely based on the user's current location, has raised an extreme concern over location privacy of the user. Generalized approaches dealing with location privacy, referred to as cloaking and obfuscation, are mainly based on a trusted third party, in which all the data remain available at a central server and thus complete knowledge of the query exists at the central node. This is the major limitation of such approaches; on the other hand, in trusted third-party-free framework clients collaborate with each other and freely communicate with the service provider without any third-party involvement. Measuring and evaluating trust among peers is a crucial aspect in trusted third-party-free framework. This paper exploits the merits and mitigating the shortcomings of both of these approaches. We propose a hybrid solution, HYB, to achieve location privacy for the mobile users who use location services frequently. The proposed HYB scheme is based on the collaborative preprocessing of location data and utilizes the benefits of homomorphic encryption technique. Location privacy is achieved at two levels, namely, at the proximity level and at distant level. The proposed HYB solution preserves the user's location privacy effectively under specific, pull-based, sporadic query scenario.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Location Privacy Solution for Mobile LBS

Gupta

Rao

2017

Mobile Information Systems

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“…Authors in [7,8] introduce methods that use PIR to ensure location privacy. Most of techniques used to retrieve information privately from databases could be treated as black boxes and therefore other mapping techniques could be applied to the same PIR architecture.…”

Section: Related Workmentioning

confidence: 99%

MaPIR: Mapping-based private information retrieval for location privacy in LBISs

Wightman

Zurbarán

Rodríguez

et al. 2013

38th Annual IEEE Conference on Local Computer Networks - Workshops

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The popularization of Location-Based Services (LBSs) has brought along benefits for users and service provider, in terms of improved quality of existing services and a better user experience. At the same time, location privacy has become one of the most critical concerns for ensuring users' right to protection. Despite the fact that one of the best ways to protect the location information is not to reveal it, there are advantages on using this information to personalize services, however it is necessary to guarantee its protection. Private Information Retrieval is a technique that creates a common language between users and service provider so that external actors cannot understand most of the information being transferred. This paper introduces MaPIR, a mapping-based private information retrieval technique that uses mathematically generated mapping to create redundancy in order to provide multiple answers to a user with an undistinguishable location. This technique is decentralized and focuses on Point of Interest Search-based application, not on tracking services. For performance evaluation, were compared in two scenarios MaPIR, a regular spatial query and the Dummy Query technique, the results show that MaPIR takes only half the time of regular geographical queries on database processing, and 5 times less than the Dummy Query technique, while providing a similar level of redundancy.

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“…Most of the PIR-based approaches for location privacy rely on hardware-based techniques, which typically utilize a secure coprocessor (SC) at the LBS server host [2,24]. This hardware creates a computing space that is protected from the LBS, to realize query privacy.…”

Section: Pir-based Location Privacymentioning

confidence: 99%

“…This hardware creates a computing space that is protected from the LBS, to realize query privacy. A major drawback of SC-based PIR is that it requires the acquisition of specialized tamperproof hardware and it usually requires periodic reshuffling of the POIs in the database, which is a computationally expensive operation [2,26].…”

Section: Pir-based Location Privacymentioning

confidence: 99%

Achieving Efficient Query Privacy for Location Based Services

Olumofin

Tysowski

Goldberg

et al. 2010

Privacy Enhancing Technologies

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Mobile smartphone users frequently need to search for nearby points of interest from a location based service, but in a way that preserves the privacy of the users' locations. We present a technique for private information retrieval that allows a user to retrieve information from a database server without revealing what is actually being retrieved from the server. We perform the retrieval operation in a computationally efficient manner to make it practical for resource-constrained hardware such as smartphones, which have limited processing power, memory, and wireless bandwidth. In particular, our algorithm makes use of a variable-sized cloaking region that increases the location privacy of the user at the cost of additional computation, but maintains the same traffic cost. Our proposal does not require the use of a trusted thirdparty component, and ensures that we find a good compromise between user privacy and computational efficiency. We evaluated our approach with a proof-of-concept implementation over a commercial-grade database of points of interest. Query response time on experimental hardware dropped from between 25 and 70 seconds to less than a second for state or provincial level granularity of location privacy. We also measured the performance of our query technique on a smartphone and wireless network.

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SPIRAL: A Scalable Private Information Retrieval Approach to Location Privacy

Cited by 35 publications

References 13 publications

A Hybrid Location Privacy Solution for Mobile LBS

A Hybrid Location Privacy Solution for Mobile LBS

MaPIR: Mapping-based private information retrieval for location privacy in LBISs

Achieving Efficient Query Privacy for Location Based Services

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