Preventing Location-Based Identity Inference in Anonymous Spatial Queries

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of Location-Based Services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to Location-Based Services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users

Preserving Co-Location Privacy in Geo-Social Networks

The number of people on social networks has grown exponentially. Users share very large volumes of personal informations and content every days. This content could be tagged with geo-spatial and temporal coordinates that may be considered sensitive for some users. While there is clearly a demand for users to share this information with each other, there is also substantial demand for greater control over the conditions under which their information is shared. Content published in a geo-aware social networks (GeoSN) often involves multiple users and it is often accessible to multiple users, without the publisher being aware of the privacy preferences of those users. This makes difficult for GeoSN users to control which information about them is available and to whom it is available. Thus, the lack of means to protect users privacy scares people bothered about privacy issues. This paper addresses a particular privacy threats that occur in GeoSNs: the Co-location privacy threat. It concerns the availability of information about the presence of multiple users in a same locations at given times, against their will. The challenge addressed is that of supporting privacy while still enabling useful services.Comment: 10 pages, 5 figure

De-anonymizable location cloaking for privacy-controlled mobile systems

The rapid technology upgrades of mobile devices and the popularity of wireless networks significantly drive the emergence and development of Location-based Services (LBSs), thus greatly expanding the business of online services and enriching the user experience. However, the personal location data shared with the service providers also leave hidden risks on location privacy. Location anonymization techniques transform the exact location of a user into a cloaking area by including the locations of multiple users in the exposed area such that the exposed location is indistinguishable from that of the other users. However in such schemes, location information once perturbed cannot be recovered from the cloaking region and as a result, users of the location cannot obtain fine granular information even when they have access to it. In this paper, we propose Dynamic Reversible Cloaking (DRC) a new de-anonymziable location cloaking mechanism that allows to restore the actual location from the perturbed information through the use of an anonymization key. Extensive experiments using realistic road network traces show that the proposed scheme is efficient, effective and scalable

Search Me If You Can: Privacy-preserving Location Query Service

Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users. Many LBS providers use users' location information to offer them convenience and useful functions. However, the LBS could greatly breach personal privacy because location itself contains much information. Hence, preserving location privacy while achieving utility from it is still an challenging question now. This paper tackles this non-trivial challenge by designing a suite of novel fine-grained Privacy-preserving Location Query Protocol (PLQP). Our protocol allows different levels of location query on encrypted location information for different users, and it is efficient enough to be applied in mobile platforms.Comment: 9 pages, 1 figure, 2 tables, IEEE INFOCOM 201

Efficient tree structured algorithm for providing confidentiality of location data to minimize communication overhead in LBS Services

We present an effective and protection safeguarding polygons spatial inquiry structure for area based administrations, called Polaris. With Polaris, the LBS supplier redistributes the encoded LBS information to cloud server, and the enrolled client can question any polygon range to get precise LBS results without revealing his/her inquiry data to the LBS supplier and cloud server. Proficient uncommon polygons spatial inquiry calculation over ciphertext is developed dependent on an enhanced homomorphic encryption innovation over Composite request gathering. With SPSQ, Polaris can look re-appropriated scrambled LBS information in cloud server by the encoded demand, and react the scrambled polygons spatial question results precisely

SECURED PRIVACY AND SHARE WITH THE DESTINATION IN CLOUD

Cloud computing refers to applications and services that run on a distributed network using virtualized resources and accessed by common Internet protocols and networking standards. Cloud computing takes the technology, services, and applications that are similar to those on the Internet and turns them into a self-service utility. Without adequate privacy protection, the system can be easily misused, e.g., to track users target them for home invasion. introduce LocX, a novel alternative that provides significantly-improved location privacy without adding uncertainty into query results or relying on strong assumptions about server security. Our key aim is to apply secure user-specific, distance-preserving coordinate transformations to all location data shared with the server. The friends of a user share this user’s secrets, so they can apply the same transformation to destination. This allows all location queries to be evaluated correctly by the server, but our privacy mechanisms guarantee that servers are unable to see or infer the actual location data from the transformed data or from the data access. show that LocX provides privacy even against a powerful adversary model, and we use prototype measurements to show that it provides privacy with very little performance overhead, making it suitable for today’s mobile devices