Location-based services are increasingly used in our daily activities. In current services, users however have to give up their location privacy in order to acquire the service. The literature features a large number of contributions which aim at enhancing user privacy in location-based services. Most of these contributions obfuscate the locations of users using spatial and or temporal cloaking in order to provide k-anonymity. Although such schemes can indeed strengthen the location privacy of users, they often decrease the service quality and do not necessarily prevent the possible tracking of user movements (i.e., direction, trajectory, velocity). With the rise of Geofencing applications, tracking of movements becomes more evident since, in these settings, the service provider is not only requesting a single location of the user, but requires the movement vectors of users to determine whether the user has entered exited a Geofence of interest. In this paper, we propose a novel solution, PrivLoc, which enables the privacy-preserving outsourcing of Geofencing and location-based services to the cloud without leaking any meaningful information about the location, trajectory, and velocity of the users. Notably, PrivLoc enables an efficient and privacy-preserving intersection of movement vectors with any polygon of interest, leveraging functionality from existing Geofencing services or spatial databases. We analyze the security and privacy provisions of PrivLoc and we evaluate the performance of our scheme by means of implementation. Our results show that the performance overhead introduced by PrivLoc can be largely tolerated in realistic deployment settings.
In what follows, we briefly overview existing contributions in the area. @cite_14 , Barkkuus and Dey show that users were concerned about the ability of services to track them.
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Although some users might willingly subscribe to location-tracking services, few would be comfortable having their location known in all situations. The authors investigate disclosure-control algorithms that hide users' positions in sensitive areas and withhold path information that indicates which areas they have visited. 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. Many web services aim to track clients as a basis for analyzing their behavior and providing personalized services. Despite much debate regarding the collection of client information, there have been few quantitative studies that analyze the effectiveness of host-tracking and the associated privacy risks. In this paper, we perform a large-scale study to quantify the amount of information revealed by common host identifiers. We analyze month-long anonymized datasets collected by the Hotmail web-mail service and the Bing search engine, which include millions of hosts across the global IP address space. In this setting, we compare the use of multiple identifiers, including browser information, IP addresses, cookies, and user login IDs. We further demonstrate the privacy and security implications of host-tracking in two contexts. In the first, we study the causes of cookie churn in web services, and show that many returning users can still be tracked even if they clear cookies or utilize private browsing. In the second, we show that host-tracking can be leveraged to improve security. Specifically, by aggregating information across hosts, we uncover a stealthy malicious attack associated with over 75,000 bot accounts that forward cookies to distributed locations. This work was done while Ting-Fang was an intern at Microsoft Research. Martin Abadi is also affiliated with the University of California, Santa Cruz. In this paper, we highlight a potential privacy threat in the current smartphone platforms, which allows any third party to collect a snapshot of installed applications without the user's consent. This can be exploited by third parties to infer various user attributes similar to what is done through tracking. We show that using only installed apps, user's gender, a demographic attribute that is frequently used in targeted advertising, can be instantly predicted with an accuracy around 70 , by training a classifier using established supervised learning techniques.