Location-based services are increasingly used in our daily activities. In current services, users however have to give up their location privacy in order to acquire the service. The literature features a large number of contributions which aim at enhancing user privacy in location-based services. Most of these contributions obfuscate the locations of users using spatial and or temporal cloaking in order to provide k-anonymity. Although such schemes can indeed strengthen the location privacy of users, they often decrease the service quality and do not necessarily prevent the possible tracking of user movements (i.e., direction, trajectory, velocity). With the rise of Geofencing applications, tracking of movements becomes more evident since, in these settings, the service provider is not only requesting a single location of the user, but requires the movement vectors of users to determine whether the user has entered exited a Geofence of interest. In this paper, we propose a novel solution, PrivLoc, which enables the privacy-preserving outsourcing of Geofencing and location-based services to the cloud without leaking any meaningful information about the location, trajectory, and velocity of the users. Notably, PrivLoc enables an efficient and privacy-preserving intersection of movement vectors with any polygon of interest, leveraging functionality from existing Geofencing services or spatial databases. We analyze the security and privacy provisions of PrivLoc and we evaluate the performance of our scheme by means of implementation. Our results show that the performance overhead introduced by PrivLoc can be largely tolerated in realistic deployment settings.
Most privacy-enhancing solutions for location-based services rely on a trusted location anonymizer'' service which hides the location of users. These services either provide @math -anonymity @cite_13 @cite_6 @cite_32 @cite_25 @cite_5 or spatial temporal cloaking with an area of interest @cite_9 @cite_28 @cite_10 @cite_12 @cite_11 . A number of solutions rely on inserting fake queries in order to prevent a database server from learning the actual location reports (e.g., @cite_23 ). While these solutions provide @math -anonymity, they incur significant additional costs on the database server. Other solutions rely on location perturbation obfuscation; these solutions map the location reports to a set of pre-defined landmarks @cite_4 or blur the user location into a spatial area using linear transformations @cite_28 @cite_10 @cite_12 @cite_24 @cite_19 Such solutions indeed hide the location of users but might affect the accuracy of the location-based service. Moreover, these solutions can only hide the location of a user, but do not aim at hiding the user movement.
@cite_4 @cite_28 @cite_10 @cite_9 @cite_32 @cite_6 @cite_24 @cite_19 @cite_23 @cite_5 @cite_13 @cite_25 @cite_12 @cite_11
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This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries. Mobile devices equipped with positioning capabilities (e.g., GPS) can ask location-dependent queries to Location Based Services (LBS). To protect privacy, the user location must not be disclosed. Existing solutions utilize a trusted anonymizer between the users and the LBS. This approach has several drawbacks: (i) All users must trust the third party anonymizer, which is a single point of attack. (ii) A large number of cooperating, trustworthy users is needed. (iii) Privacy is guaranteed only for a single snapshot of user locations; users are not protected against correlation attacks (e.g., history of user movement). We propose a novel framework to support private location-dependent queries, based on the theoretical work on Private Information Retrieval (PIR). Our framework does not require a trusted third party, since privacy is achieved via cryptographic techniques. Compared to existing work, our approach achieves stronger privacy for snapshots of user locations; moreover, it is the first to provide provable privacy guarantees against correlation attacks. We use our framework to implement approximate and exact algorithms for nearest-neighbor search. We optimize query execution by employing data mining techniques, which identify redundant computations. Contrary to common belief, the experimental results suggest that PIR approaches incur reasonable overhead and are applicable in practice. 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. With advances in sensing and positioning technology, fueled by the ubiquitous deployment of wireless networks, location-aware computing has become a fundamental model for offering a wide range of life enhancing services. However, the ability to locate users and mobile objects opens doors for new threats - the intrusion of location privacy. Location anonymization refers to the process of perturbing the exact location of users as a cloaking region such that a user's location becomes indistinguishable from the location of a set of other users. A fundamental limitation of existing location anonymization techniques is that location information once perturbed to provide a certain anonymity level cannot be reversed to reduce anonymity or the degree of perturbation. This is especially a serious limiting factor in multi-level privacy-controlled scenarios where different users of the location information have different levels of access. This paper presents ReverseCloak, a new class of reversible location cloaking mechanisms that effectively support multi-level location privacy, allowing selective de-anonymization of the cloaking region to reduce the granularity of the perturbed location when suitable access credentials are provided. We evaluate the ReverseCloak techniques through extensive experiments on realistic road network traces generated by GTMobiSim. Our experiments show that the proposed techniques are efficient, scalable and provide the required level of privacy.