Xihui Chen

Protecting query privacy in location-based services

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 privacy-preserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts—user profiles 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 expressed in these metrics. By experiments, our metrics and algorithms are shown to be effective and efficient for practical usage.

Measuring User Similarity with Trajectory Patterns: Principles and New Metrics

The accumulation of users’ whereabouts in location-based applications has made it possible to construct user mobility profiles. Trajectory patterns, i.e., traces of places of interest that a user frequently visits, are among the most popular models of mobility profiles. In this paper, we revisit measuring user similarity using trajectory patterns, which is an important supplement for friend recommendation in on-line social networks. Specifically, we identify and formalise a number of basic principles that should hold when quantifying user similarity with trajectory patterns. These principles allow us to evaluate existing metrics in the literature and demonstrate their insufficiencies. Then we propose for the first time a new metric that respects all the identified principles. The metric is extended to deal with location semantics. Through experiments on a real-life trajectory dataset, we show the effectiveness of our new metrics.

Improving Automatic Verification of Security Protocols with XOR

Kusters and Truderung recently proposed an automatic verification method for security protocols with exclusive or (XOR). Their method reduces protocols with XOR to their XOR-free equivalents, enabling efficient verification by tools such as ProVerif. Although the proposed method works efficiently for verifying secrecy, verification of authentication properties is inefficient and sometimes impossible. In this paper, we improve the work by Kusters and Truderung in two ways. First, we extend their method for authentication verification to a richer class of XOR-protocols by automatically introducing bounded verification. Second, we improve the efficiency of their approach by developing a number of dedicated optimizations. We show the applicability of our work by implementing a prototype and applying it to both existing benchmarks and RFID protocols. The experiments show promising results and uncover a flaw in a recently proposed RFID protocol.

A Trust Framework for Evaluating GNSS Signal Integrity

Through real-life experiments, it has been proved, not only in theory but also in practice, that civil signals of Global Navigation Satellite Systems (GNSS) can be spoofed. Consequently, a number of spoofing detection techniques have been proposed to verify the integrity of GNSS signals. In this paper, we develop a novel trust framework based on subjective logic to evaluate the integrity of received GNSS civil signals. We formally define signal integrity for the first time in the framework and use it to precisely characterise different spoofing detection methods. Our framework captures the uncertainty during the inference of signal integrity which has been largely ignored or not explicitly specified in the literature. Our framework also gives rise to several natural ways to combine the outputs of various spoofing detection methods on signal integrity. We validate our framework through experiments using both real and simulated signals and the results show that our framework is effective.

Implementation and validation of a Localisation Assurance service provider

Existing Global Navigation Satellite Systems offer no authentication to the open service signals and so stand-alone receivers are vulnerable to meaconing and spoofing attacks. These attacks interfere with the integrity and authenticity of satellite signals: they can delay signals, or re-broadcast signals. Positioning is thus compromised and location-based services are at risk. This paper describes a solution to mitigate this risk. It is a trusted third-party Localisation Assurance service that informs location-based services providers up to which level a location claimed by client can be trusted. It runs several tests over the localisation data of client receivers and certifies the level of assurance of locations. An assurance level expresses the amount of trust the third-party has that a receivers location is calculated from integral and authentic satellite signals.

DEMO: Demonstrating a trust framework for evaluating GNSS signal integrity

Through real-life experiments, it has been proved, not only in theory but also in practice, that civil signals of Global Navigation Satellite Systems (GNSS) can be spoofed. Consequently, a number of spoofing detection techniques have been proposed to verify the integrity of GNSS signals. In this paper, we develop a novel trust framework based on subjective logic to evaluate the integrity of received GNSS civil signals. We formally define signal integrity for the first time in the framework and use it to precisely characterise different spoofing detection methods. Our framework captures the uncertainty during the inference of signal integrity which has been largely ignored or not explicitly specified in the literature. Our framework also gives rise to several natural ways to combine the outputs of various spoofing detection methods on signal integrity. We validate our framework through experiments using both real and simulated signals and the results show that our framework is effective.

Post-hoc User Traceability Analysis in Electronic Toll Pricing Systems

Electronic Toll Pricing (ETP), a location-based vehicular service, allows users to pay tolls without stopping or even slowing down their cars. User location records are collected so as to calculate their payments. However, users have privacy concerns as locations are considered as private information. In this paper, we focus on user traceability in ETP systems where anonymous location records are stored by the service providers. Based on user toll payment information, we propose a post-hoc analysis of user traceability, which aims at computing a user’s all possible traces. Moreover, we propose several methods to improve the effectiveness of the analysis by combining other contextual information and propose a number of optimisations to improve its efficiency as well. We develop a prototype and evaluate the effectiveness of the analysis by conducting extensive experiments on a number of simulated datasets.