The progress of location-based services has led to serious concerns on location privacy leakage. For effective and efficient location privacy preservation (LPP), existing methods are still not fully competent. They are often vulnerable under the identification attack with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection efficacy and low computational complexity simultaneously. We propose a LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them through synthesizing (AIs). The AIs refer to the synthesized traces which have similar semantic features to the actual traces, and do contain any target location. Two dedicated techniques are devised: the and . They play significant roles in two critical steps of synthesizing AIs. We conduct experiments on real datasets in two cities (Shanghai, China and Asturias, Spain) to validate the high efficacy and scalability of the proposed method. In these two datasets, the experimental results show that our method achieves the preservation efficacy of @math and @math , and its run time of building the generators is only @math and @math seconds, respectively. This study would give the research community new insights into improving the practicality of the state-of-the-art LPP paradigm via counterfeiting locations.
The second type of techniques is devised to protect the privacy of the statistic database of location data. The state-of-the-art technique is differential privacy @cite_9 @cite_34 @cite_6 . Its goal is to protect an individual's data while publishing aggregate information about the database. @cite_9 proposed to achieve Geo-Indistinguishability by adding noise drawn from a Laplace distribution. @cite_6 proposed a new probabilistic differential Privacy-preserving location recommendation framework. They used it to achieve the trade-off between high recommendation accuracy and strict location privacy for check-in dataset.
@cite_9 @cite_34 @cite_6
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Radio-frequency identification devices (RFID) may emerge as one of the most pervasive computing technologies in history. On the one hand, with tags affixed to consumer items as well as letters, packets or vehicles costs in the supply chain can be greatly reduced and new applications introduced. On the other hand, unique means of identification in each tag like serial numbers enable effortless traceability of persons and goods. But data protection and privacy are worthwhile civil liberties. We introduce a simple scheme relying on one way hash-functions that greatly enhances location privacy by changing traceable identifiers on every read getting by with only a single, unreliable message exchange. Thereby the scheme is safe from many threats like eavesdropping, message interception, spoofing, and replay attacks. A location proximity test service allows mobile users to determine whether they are in close proximity to each other, and has found numerous applications in mobile social networks. Unfortunately, existing solutions usually reveal much of users’ private location information during a proximity test. They are also vulnerable to location cheating where an attacker reports false locations to gain an advantage. Moreover, the initial trust establishment among unfamiliar users in large scale mobile social networks has been a challenging task. In this paper, we propose a novel scheme that enables a user to perform (1) a location based handshake that establishes secure communications among strangers, who do not have a pre-shared secret, and (2) a privacy-preserving proximity test without revealing the user’s actual location to the server or other users not within the proximity. The proposed scheme is based on a novel concept, i.e., spatial-temporal location tags, and we put forward a location tag construction method using environmental signals that provides an unforgeable location proof. We use Bloom filters to efficiently represent users’ location tags and vicinity regions. We exploit fuzzy extractor, a lightweight cryptographic primitive, to extract shared secrets between matching location tags. We conduct extensive analysis, simulation, and real experiments to demonstrate the feasibility, security, and efficiency of our scheme.