Geosocial networks are online social networks centered on the locations of subscribers and businesses. Providing input to targeted advertising, profiling social network users becomes an important source of revenue. Its natural reliance on personal information introduces a trade-off between user privacy and incentives of participation for businesses and geosocial network providers. In this paper we introduce location centric profiles (LCPs), aggregates built over the profiles of users present at a given location. We introduce PROFILR, a suite of mechanisms that construct LCPs in a private and correct manner. We introduce iSafe, a novel, context aware public safety application built on PROFILR . Our Android and browser plugin implementations show that PROFILR is efficient: the end-to-end overhead is small even under strong correctness assurances.
Location and temporal cloaking techniques, or introducing errors in reported locations in order to provide 1-out-of-k anonymity have been initially proposed in @cite_31 , followed by a significant body of work @cite_3 @cite_14 @cite_17 @cite_38 . We note that provides an orthogonal notion of @math -anonymity: instead of reporting intervals containing @math other users, we allow the construction of location centric profiles only when @math users have reported their location. Computed LCPs hide the profiles the users: user profiles are anonymous, only aggregates are available for inspection, and interactions with venues and the provider are indistinguishable.
@cite_38 @cite_14 @cite_3 @cite_31 @cite_17
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This article examines a new problem of k-anonymity with respect to a reference dataset in privacy-aware location data publishing: given a user dataset and a sensitive event dataset, we want to generalize the user dataset such that by joining it with the event dataset through location, each event is covered by at least k users. Existing k-anonymity algorithms generalize every k user locations to the same vague value, regardless of the events. Therefore, they tend to overprotect against the privacy compromise and make the published data less useful. In this article, we propose a new generalization paradigm called local enlargement, as opposed to conventional hierarchy- or partition-based generalization. Local enlargement guarantees that user locations are enlarged just enough to cover all events k times, and thus maximize the usefulness of the published data. We develop an O(Hn)-approximate algorithm under the local enlargement paradigm, where n is the maximum number of events a user could possibly cover and Hn is the Harmonic number of n. With strong pruning techniques and mathematical analysis, we show that it runs efficiently and that the generalized user locations are up to several orders of magnitude smaller than those by the existing algorithms. In addition, it is robust enough to protect against various privacy attacks. The continuous advances in sensing and positioning technologies have resulted in a dramatic increase in popularity of Location-Based Services (LBS). Nevertheless, the LBS can lead to user privacy breach due to sharing location information with potentially malicious services. A high degree of location privacy preservation for LBS is extremely required. In this paper, a clustering K-anonymity scheme for location privacy preservation (namely CK) is proposed. The CK scheme does not rely on a trusted third party to anonymize the location information of users. In CK scheme, the whole area that all the users reside is divided into clusters recursively in order to get cloaked area. The exact location information of the user is replaced by the cloaked spatial temporal boundary (STB) including K users. The user can adjust the resolution of location information with spatial or temporal constraints to meet his personalized privacy requirement. The experimental results show that CK can provide stringent privacy guarantees, strong robustness and high QoS (Quality of Service). There is a rich collection of literature that aims at protecting the privacy of users querying location-based services. One of the most popular location privacy techniques consists in cloaking users' locations such that k users appear as potential senders of a query, thus achieving k-anonymity. This paper analyzes the effectiveness of k-anonymity approaches for protecting location privacy in the presence of various types of adversaries. The unraveling of the scheme unfolds the inconsistency between its components, mainly the cloaking mechanism and the k-anonymity metric. We show that constructing cloaking regions based on the users' locations does not reliably relate to location privacy, and argue that this technique may even be detrimental to users' location privacy. The uncovered flaws imply that existing k-anonymity scheme is a tattered cloak for protecting location privacy. Advances in sensing and tracking technology enable location-based applications but they also create significant privacy risks. Anonymity can provide a high degree of privacy, save service users from dealing with service providers’ privacy policies, and reduce the service providers’ requirements for safeguarding private information. However, guaranteeing anonymous usage of location-based services requires that the precise location information transmitted by a user cannot be easily used to re-identify the subject. This paper presents a middleware architecture and algorithms that can be used by a centralized location broker service. The adaptive algorithms adjust the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area. Using a model based on automotive traffic counts and cartographic material, we estimate the realistically expected spatial resolution for different anonymity constraints. The median resolution generated by our algorithms is 125 meters. Thus, anonymous location-based requests for urban areas would have the same accuracy currently needed for E-911 services; this would provide sufficient resolution for wayfinding, automated bus routing services and similar location-dependent services.