Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Location Privacy in the Era of Big Data and Machine Learning

Location data of individuals is one of the most sensitive sources of information that once revealed to ill-intended individuals or service providers, can cause severe privacy concerns. In this thesis, we aim at preserving the privacy of users in telecommunication networks against untrusted service providers as well as improving their privacy in the publication of location datasets. For improving the location privacy of users in telecommunication networks, we consider the movement of users in trajectories and investigate the threats that the query history may pose on location privacy. We develop an attack model based on the Viterbi algorithm termed as Viterbi attack, which represents a realistic privacy threat in trajectories. Next, we propose a metric called transition entropy that helps to evaluate the performance of dummy generation algorithms, followed by developing a robust dummy generation algorithm that can defend users against the Viterbi attack. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset. For privacy preserving data publishing, an enhanced framework for anonymization of spatio-temporal trajectory datasets termed the machine learning based anonymization (MLA) is proposed. The framework consists of a robust alignment technique and a machine learning approach for clustering datasets. The framework and all the proposed algorithms are applied to the Geolife dataset, which includes GPS logs of over 180 users in Beijing, China

Users Collaborative Mix-Zone to Resist the Query Content and Time Interval Correlation Attacks

In location-based services of continuous query, it is easier than snapshot to confirm whether a location belongs to a particular user, because sole location can be composed into a trajectory by profile correlation. In order to cut off the correlation and disturb the sub-trajectory, an un-detective region called mix-zone was proposed. However, at the time of this writing, the existing algorithms of this type mainly focus on the profiles of ID, passing time, transition probability, mobility patterns as well as road characteristics. In addition, there is still no standard way of coping with attacks of correlating each location by mining out query content and time interval from the sub-trajectory. To cope with such types of attack, users have to generalize their query contents and time intervals similarity. Hence, this paper first provided an attack model to simulate the adversary correlating the real location with a higher probability of query content and time interval similarity. Then a user collaboration mix-zone (CoMix) that can generalize these two types of profiles is proposed, so as to achieve location privacy. In CoMix, each user shares the common profile set to lowering the probability of success opponents to get the actual position through the correlation of location. Thirdly, entropy is utilized to measure the level of privacy preservation. At last, this paper further verifies the effectiveness and efficiency of the proposed algorithm by experimental evaluations

An Approach for Ensuring Robust Support for Location Privacy and Identity Inference Protection

The challenge of preserving a user\u27s location privacy is more important now than ever before with the proliferation of handheld devices and the pervasive use of location based services. To protect location privacy, we must ensure k-anonymity so that the user remains indistinguishable among k-1 other users. There is no better way but to use a location anonymizer (LA) to achieve k-anonymity. However, its knowledge of each user\u27s current location makes it susceptible to be a single-point-of-failure. In this thesis, we propose a formal location privacy framework, termed SafeGrid that can work with or without an LA. In SafeGrid, LA is designed in such a way that it is no longer a single point of failure. In addition, it is resistant to known attacks and most significantly, the cloaking algorithm it employs meets reciprocity condition. Simulation results exhibit its better performance in query processing and cloaking region calculation compared with existing solutions. In this thesis, we also show that satisfying k-anonymity is not enough in preserving privacy. Especially in an environment where a group of colluded service providers collaborate with each other, a user\u27s privacy can be compromised through identity inference attacks. We present a detailed analysis of such attacks on privacy and propose a novel and powerful privacy definition called s-proximity. In addition to building a formal definition for s-proximity, we show that it is practical and it can be incorporated efficiently into existing systems to make them secure

Privacy and trustworthiness management in moving object environments

The use of location-based services (LBS) (e.g., Intel\u27s Thing Finder) is expanding. Besides the traditional centralized location-based services, distributed ones are also emerging due to the development of Vehicular Ad-hoc Networks (VANETs), a dynamic network which allows vehicles to communicate with one another. Due to the nature of the need of tracking users\u27 locations, LBS have raised increasing concerns on users\u27 location privacy. Although many research has been carried out for users to submit their locations anonymously, the collected anonymous location data may still be mapped to individuals when the adversary has related background knowledge. To improve location privacy, in this dissertation, the problem of anonymizing the collected location datasets is addressed so that they can be published for public use without violating any privacy concerns. Specifically, a privacy-preserving trajectory publishing algorithm is proposed that preserves high data utility rate. Moreover, the scalability issue is tackled in the case the location datasets grows gigantically due to continuous data collection as well as increase of LBS users by developing a distributed version of our trajectory publishing algorithm which leveraging the MapReduce technique. As a consequence of users being anonymous, it becomes more challenging to evaluate the trustworthiness of messages disseminated by anonymous users. Existing research efforts are mainly focused on privacy-preserving authentication of users which helps in tracing malicious vehicles only after the damage is done. However, it is still not sufficient to prevent malicious behavior from happening in the case where attackers do not care whether they are caught later on. Therefore, it would be more effective to also evaluate the content of the message. In this dissertation, a novel information-oriented trustworthiness evaluation is presented which enables each individual user to evaluate the message content and make informed decisions --Abstract, page iii

Privacy Preserving User Data Publication In Social Networks

Recent trends show that the popularity of Social Networks (SNs) has been increasing rapidly. From daily communication sites to online communities, an average person\u27s daily life has become dependent on these online networks. Additionally, the number of people using at least one of the social networks have increased drastically over the years. It is estimated that by the end of the year 2020, one-third of the world\u27s population will have social accounts. Hence, user privacy protection has gained wide acclaim in the research community. It has also become evident that protection should be provided to these networks from unwanted intruders. In this dissertation, we consider data privacy on online social networks at the network level and the user level. The network-level privacy helps us to prevent information leakage to third-party users like advertisers. To achieve such privacy, we propose various schemes that combine the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We combine the concepts of k-anonymity and l-diversity to achieve user privacy. To provide user-level privacy, we consider the scenario of mobile social networks as the user location privacy is the much-compromised problem. We provide a distributed solution where users in an area come together to achieve their desired privacy constraints. We also consider the mobility of the user and the network to provide much better results