Privacy in Georeferenced Context-Aware Services: A Survey

Riboni, Daniele; Pareschi, Linda; Bettini, Cláudio

doi:10.1007/978-3-642-03511-1_7

Cited by 32 publications

(22 citation statements)

References 52 publications

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“…The common criticism against this PIR-based approach in the literature is that it is too costly to be practical [29], and that the computational overhead is unsuitable for resource-constrained hardware, such as smartphones [35].…”

Section: Pir-based Location Privacymentioning

confidence: 99%

“…The authors' own optimization of PIR in [18] (paper previously mentioned above) reuses partial computation results (i.e., multiplications of large numbers) and parallelizes the computations. This optimization reduces CPU cost by 40%, but the overall query response time is still impractical [29,35]. Ghinita [16] suggests improving the performance of PIRbased techniques for LBS privacy through a hybrid method that includes a PIR phase on a restricted subset of the data space.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

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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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Section: Pir-based Location Privacymentioning

confidence: 99%

Section: Hybrid Techniquesmentioning

confidence: 99%

Achieving Efficient Query Privacy for Location Based Services

Olumofin

Tysowski

Goldberg

et al. 2010

Privacy Enhancing Technologies

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“…Some users are eliminated from the set of potential issuers as the algorithms cannot output the same generalised region to all of them when they issue the request. Such information is classified as contextual information in the literature and the privacy in LBSs related to contextual information has been recognised as context-aware privacy [37]. Many types of contextual information have been studied so far.…”

Section: Introductionmentioning

confidence: 99%

Protecting query privacy in location-based services

Chen

Pang

2013

Geoinformatica

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The popularity of location-based services (LBSs) leads to severe concerns on users' privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacypreserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts-user prof iles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users' query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users' query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users' privacy requirements This article is a revised and extended version of our two conference papers [7,8].Xihui Chen is supported by an AFR PhD grant from the National Research Fund, Luxembourg.

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“…Existing trust management solutions focus on at most one trust aspect (e.g. identity [3], roles [31], or privacy [108,34,83,78,79]), and do not provide support for trustworthiness assessment when more trust aspects are combined or when user goals (e.g. privacy or context-based adaptation) should be considered in order to tailor the trust assessment strategy.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…These identity management solutions allow users to choose one of their identities and configure which services are authorized to access their profile information. Some of the existing solutions also provide support for full or partial anonymization of the users' identity and context information [10,108]. These solutions contribute to reduce the privacy risks for service users.…”

Section: Introductionmentioning

confidence: 99%

Trust and privacy management support for context-aware service platforms

Neisse¹

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In a context-aware service platform, service providers adapt their services to the current situation of the service users using context information retrieved from context information providers. In such a service provisioning platform, important trust and privacy issues arise, because different entities responsible for different tasks have to collaborate in the provisioning of the services. Context information is privacy sensitive by nature, making the communication and processing of this information a potential privacy threat. The main goal of this thesis is to learn how to support users and providers of context-aware services in managing the trade-off between privacy protection and context-based service adaptation. More and more precise context information retrieved from trustworthy context information providers allows context-aware service provider to adapt their services more reliably. However, more and more precise context information also means a higher risk for the service users in case of a privacy violation

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Privacy in Georeferenced Context-Aware Services: A Survey

Cited by 32 publications

References 52 publications

Achieving Efficient Query Privacy for Location Based Services

Achieving Efficient Query Privacy for Location Based Services

Protecting query privacy in location-based services

Trust and privacy management support for context-aware service platforms

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