ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs

The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal

Exploring historical location data for anonymity preservation in location-based services

We present a new approach for K-anonymity protection in Location-Based Services (LBSs). Specifically, we depersonalize location information by ensuring that each location reported for LBSs is a cloaking area that contains K different footprints--- historical locations of different mobile nodes. Therefore, the exact identity and location of the service requestor remain anonymous from LBS service providers. Existing techniques, on the other hand, compute the cloaking area using current locations of K neighboring hosts of the service requestor. Because of this difference, our approach significantly reduces the cloaking area, which in turn decreases query processing and communication overhead for returning query results to the requesting host. In addition, existing techniques also require frequent location updates from all nodes, regardless of whether or not these nodes are requesting LBSs. Most importantly, our approach is the first practical solution that provides K-anonymity trajectory protection needed to ensure anonymity when the mobile host requests LBSs continuously as it moves. Our solution depersonalizes a user\u27s trajectory (a time-series of the user\u27s locations) based on the historical trajectories of other users

Location Privacy and Its Applications: A Systematic Study

© 2013 IEEE. This paper surveys the current research status of location privacy issues in mobile applications. The survey spans five aspects of study: the definition of location privacy, attacks and adversaries, mechanisms to preserve the privacy of locations, location privacy metrics, and the current status of location-based applications. Through this comprehensive review, all the interrelated aspects of location privacy are integrated into a unified framework. Additionally, the current research progress in each area is reviewed individually, and the links between existing academic research and its practical applications are identified. This in-depth analysis of the current state-of-play in location privacy is designed to provide a solid foundation for future studies in the field

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

Entropy-based privacy against profiling of user mobility

Location-based services (LBSs) flood mobile phones nowadays, but their use poses an evident privacy risk. The locations accompanying the LBS queries can be exploited by the LBS provider to build the user profile of visited locations, which might disclose sensitive data, such as work or home locations. The classic concept of entropy is widely used to evaluate privacy in these scenarios, where the information is represented as a sequence of independent samples of categorized data. However, since the LBS queries might be sent very frequently, location profiles can be improved by adding temporal dependencies, thus becoming mobility profiles, where location samples are not independent anymore and might disclose the user's mobility patterns. Since the time dimension is factored in, the classic entropy concept falls short of evaluating the real privacy level, which depends also on the time component. Therefore, we propose to extend the entropy-based privacy metric to the use of the entropy rate to evaluate mobility profiles. Then, two perturbative mechanisms are considered to preserve locations and mobility profiles under gradual utility constraints. We further use the proposed privacy metric and compare it to classic ones to evaluate both synthetic and real mobility profiles when the perturbative methods proposed are applied. The results prove the usefulness of the proposed metric for mobility profiles and the need for tailoring the perturbative methods to the features of mobility profiles in order to improve privacy without completely loosing utility.This work is partially supported by the Spanish Ministry of Science and Innovation through the CONSEQUENCE (TEC2010-20572-C02-01/02) and EMRISCO (TEC2013-47665-C4-4-R) projects.The work of Das was partially supported by NSF Grants IIS-1404673, CNS-1355505, CNS-1404677 and DGE-1433659. Part of the work by Rodriguez-Carrion was conducted while she was visiting the Computer Science Department at Missouri University of Science and Technology in 2013–2014

Privacy-Preserved Linkable Social-Physical Data Publication

In this dissertation, we investigate the privacy-preserved data publication problems towards pervasively existing linkable social-physical contents. On the one hand, data publication has been considered as a critical approach to facilitate numerous utilities for individuals, populations, platform owners, and all third-party service providers. On the other hand, the unprecedented adoption of mobile devices and the dramatic development of Internet-of-Thing (IoT) systems have pushed the collection of surrounding physical information among populations to a totally novel stage. The collected contents can provide a fine-grained access to both physical and social aspects of the crowds, which introduces a comprehensively linkable and potentially sensitive information domain. The linkage includes the related index like privacy, utility, and efficiency for sophisticated applications, the inherent correlations among multiple data sources or information dimensions, and the connections among individuals. As the linkage leads to various novel challenges for privacy preservation, there should be a body of novel mechanisms for linkable social-physical data publications. As a result, this dissertation proposes a series of mechanisms for privacy-preserved linkable social-physical data publication. Firstly, we study the publication of physical data where the co-existing useful social proles and the sensitive physical proles of the data should be carefully maintained. Secondly, we investigate the data publication problem jointly considering the privacy preservation, data utility, and resource efficiency for task completion in crowd-sensing systems. Thirdly, we investigate the publication of private contents used for the recommendation, where contents of a user contribute to the recommendation results for others. Fourthly, we study the publications of reviews in local business service systems, where users expect to conceal their frequently visited locations while cooperatively maintain the utility of the whole system. Fifthly, we study the acquisition of privacy-preserved knowledge on cyber-physical social networks, where third-party service providers can derive the community structure without accessing the sensitive social links. We also provide detailed analysis and discussion for proposed mechanisms, and extensively validate their performance via real-world datasets. Both results demonstrate that the proposed mechanisms can properly preserve the privacy while maintaining the data utility. At last, we also propose the future research topics to complete the whole dissertation. The first topic focuses on the privacy preservation towards correlations beneath multiple data sources. The second topic studies more privacy issues for the whole population during data publication, including both the novel threats for related communities, and the disclosure of trends within crowds

Constructing elastic distinguishability metrics for location privacy

With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a "geographic fence" that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris' wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy