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

A Middleware for the Internet of Things

The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as things to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things, or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.Comment: 20 pages, International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.2, March 201

Context for Ubiquitous Data Management

In response to the advance of ubiquitous computing technologies, we believe that for computer systems to be ubiquitous, they must be context-aware. In this paper, we address the impact of context-awareness on ubiquitous data management. To do this, we overview different characteristics of context in order to develop a clear understanding of context, as well as its implications and requirements for context-aware data management. References to recent research activities and applicable techniques are also provided

A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., “restaurant”). Such data can reveal some personal information about users beyond what they actually expect to disclose, hence their privacy is threatened. To mitigate such threats, several privacy protection techniques based on location generalization have been proposed. Although the privacy implications of such techniques have been extensively studied, the utility implications are mostly unknown. In this paper, we propose a predictive model for quantifying the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. We first study the users’ motivations behind their location check-ins, based on a study targeted at Foursquare users (N = 77). We propose a machine-learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for the utility implications of generalization. Based on the survey data, our results show that the model accurately predicts the fine-grained motivation behind a check-in in 43% of the cases and in 63% of the cases for the coarse-grained motivation. It also predicts, with a mean error of 0.52 (on a scale from 1 to 5), the loss of utility caused by semantic and geographical generalization. This model makes it possible to design of utility-aware, privacy-enhancing mechanisms in location-based online social networks. It also enables service providers to implement location-sharing mechanisms that preserve both the utility and privacy for their users

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

Permission systems are the main defense that mobile platforms, such as Android and iOS, offer to users to protect their private data from prying apps. However, due to the tension between usability and control, such systems have several limitations that often force users to overshare sensitive data. In this work, we address some of these limitations with SmarPer, an advanced permission mechanism for Android. First, to address the rigidity of current permission systems and their poor matching of users' privacy preferences, SmarPer relies on contextual information and machine learning to predict permission decisions at runtime. Using our SmarPer implementation, we collected 8,521 runtime permission decisions from 41 participants in real conditions. Note that the goal of SmarPer is to mimic the users decisions, not to make privacy-preserving decisions per se. With this unique data set, we show that tting an efcient Bayesian linear regression model results in a mean correct classication rate of 80% (3%). This represents a mean relative improvement of 50% over a user-dened static permission policy, i.e., the model used in current permission systems. Second, SmarPer also focuses on the suboptimal trade-off between privacy and utility; instead of only “allow” or “deny” decisions, SmarPer also offers an “obfuscate” option where users can still obtain utility by revealing partial information to apps. We implemented obfuscation techniques in SmarPer for different data types and evaluated them during our data collection campaign. Our results show that 73% of the participants found obfuscation useful and it accounted for almost a third of the total number of decisions. In short, we are the first to show, using a large dataset of real in situ permission decisions, that it is possible to learn users’ unique decision patterns at runtime using contextual information while supporting data obfuscation; this an important step towards automating the management of permissions in smartphones

DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks

IEEE Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking

Differentially Private Location Privacy in Practice

With the wide adoption of handheld devices (e.g. smartphones, tablets) a large number of location-based services (also called LBSs) have flourished providing mobile users with real-time and contextual information on the move. Accounting for the amount of location information they are given by users, these services are able to track users wherever they go and to learn sensitive information about them (e.g. their points of interest including home, work, religious or political places regularly visited). A number of solutions have been proposed in the past few years to protect users location information while still allowing them to enjoy geo-located services. Among the most robust solutions are those that apply the popular notion of differential privacy to location privacy (e.g. Geo-Indistinguishability), promising strong theoretical privacy guarantees with a bounded accuracy loss. While these theoretical guarantees are attracting, it might be difficult for end users or practitioners to assess their effectiveness in the wild. In this paper, we carry on a practical study using real mobility traces coming from two different datasets, to assess the ability of Geo-Indistinguishability to protect users' points of interest (POIs). We show that a curious LBS collecting obfuscated location information sent by mobile users is still able to infer most of the users POIs with a reasonable both geographic and semantic precision. This precision depends on the degree of obfuscation applied by Geo-Indistinguishability. Nevertheless, the latter also has an impact on the overhead incurred on mobile devices resulting in a privacy versus overhead trade-off. Finally, we show in our study that POIs constitute a quasi-identifier for mobile users and that obfuscating them using Geo-Indistinguishability is not sufficient as an attacker is able to re-identify at least 63% of them despite a high degree of obfuscation.Comment: In Proceedings of the Third Workshop on Mobile Security Technologies (MoST) 2014 (http://arxiv.org/abs/1410.6674

Location privacy policy management system

The advance in wireless communication and positioning systems has permitted development of a large variety of location-based services that, for example, can help people easily locate family members or find nearest gas station or restaurant. As location-based services become more and more popular, concerns are growing about the misuse of location information by malicious parties. In order to preserve location privacy, many efforts have been devoted to preventing service providers from determining users\u27 exact locations. Few works have sought to help users manage their privacy preferences; however management of privacy is an important issue in real applications. This work developed an easy-to-use location privacy management system. Specifically, it defines a succinct yet expressive location privacy policy constructs that can be easily understood by ordinary users. The system provides various policy management functions including policy composition, policy conflict detection, and policy recommendation. Policy composition allows users to insert and delete policies. Policy conflict detection will automatically check conflict among policies whenever there is any change. The policy recommendation system will generate recommended policies based on users\u27 basic requirements in order to reduce users\u27 burden. A system prototype has been implemented and evaluated in terms of both efficiency and effectiveness --Abstract, page iii