GeoIntelligence: Data Mining Locational Social Media Content for Profiling and Information Gathering

The current social media landscape has resulted in a situation where people are encouraged to share a greater amount of information about their day-to-day lives than ever before. In this environment a large amount of personal data is disclosed in a public forum with little to no regard for the potential privacy impacts. This paper focuses on the presence of geographic data within images, metadata and individual postings. The GeoIntelligence project aims to aggregate this information to educate users on the possible implications of the utilisation of these services as well as providing service to law enforcement and business. This paper demonstrates the ability to profile users on an individual and group basis from data posted openly to social networking services

Preserving Co-Location Privacy in Geo-Social Networks

The number of people on social networks has grown exponentially. Users share very large volumes of personal informations and content every days. This content could be tagged with geo-spatial and temporal coordinates that may be considered sensitive for some users. While there is clearly a demand for users to share this information with each other, there is also substantial demand for greater control over the conditions under which their information is shared. Content published in a geo-aware social networks (GeoSN) often involves multiple users and it is often accessible to multiple users, without the publisher being aware of the privacy preferences of those users. This makes difficult for GeoSN users to control which information about them is available and to whom it is available. Thus, the lack of means to protect users privacy scares people bothered about privacy issues. This paper addresses a particular privacy threats that occur in GeoSNs: the Co-location privacy threat. It concerns the availability of information about the presence of multiple users in a same locations at given times, against their will. The challenge addressed is that of supporting privacy while still enabling useful services.Comment: 10 pages, 5 figure

GeoIntelligence: Data Mining Locational Social Media Content for Profiling and Information Gathering

The current social media landscape has resulted in a situation where people are encouraged to share a greater amount of information about their day-to-day lives than ever before. In this environment a large amount of personal data is disclosed in a public forum with little to no regard for the potential privacy impacts. This paper focuses on the presence of geographic data within images, metadata and individual postings. The GeoIntelligence project aims to aggregate this information to educate users on the possible implications of the utilisation of these services as well as providing service to law enforcement and business. This paper demonstrates the ability to profile users on an individual and group basis from data posted openly to social networking services

Location-related privacy in geo-social networks

Geo-social networks (GeoSNs) provide context-aware services that help associate location with users and content. The proliferation of GeoSNs indicates that they're rapidly attracting users. GeoSNs currently offer different types of services, including photo sharing, friend tracking, and "check-ins. " However, this ability to reveal users' locations causes new privacy threats, which in turn call for new privacy-protection methods. The authors study four privacy aspects central to these social networks - location, absence, co-location, and identity privacy - and describe possible means of protecting privacy in these circumstances

The design and implementation of a smartphone and Bluetooth-based criminal tracking system

This thesis describes the design and implementation of a real-time criminal tracking system. A criminal under tracking is asked to wear a low energy Bluetooth device and carry a smartphone. The Bluetooth device is secure on his body (e.g., hand or foot) and communicates with the smartphone, which communicates with a central server through cellular networks. The smartphone monitors the status of the Bluetooth device and reports to the server in real time when the status changes (e.g., connection lost or device being taken off). Moreover, it monitors the criminal\u27s movement and reports to the server whenever the criminal moves into an alert zone, a geographic region where the law enforcement wants the criminal\u27s movement to be tracked. Compared to the existing tracking approaches, our system has the following desired features. 1) Scalable. Instead of having a criminal to report its location all the time, the system allows one to configure where and when the criminal needs to be tracked, thus minimizing both mobile communication cost and server processing cost. 2) Low-cost. The system uses only off-shelf components (e.g., Bluetooth device and smartphone) which communicate through regular wireless networks. 3) Secure. The communication between a Bluetooth device and the corresponding smartphone is authenticated through One Time Password

Understanding and Enforcing Opacity

Abstract—This paper puts a spotlight on the specification and enforcement of opacity, a security policy for protecting sensitive properties of system behavior. We illustrate the fine granularity of the opacity policy by location privacy and privacy-preserving aggregation scenarios. We present a frame-work for opacity and explore its key differences and formal connections with such well-known information-flow models as noninterference, knowledge-based security, and declassifica-tion. Our results are machine-checked and parameterized in the observational power of the attacker, including progress-insensitive, progress-sensitive, and timing-sensitive attackers. We present two approaches to enforcing opacity: a whitebox monitor and a blackbox sampling-based enforcement. We report on experiments with prototypes that utilize state-of-the-art Satisfiability Modulo Theories (SMT) solvers and the random testing tool QuickCheck to establish opacity for the location and aggregation-based scenarios. I

Faster Privacy-Preserving Location Proximity Schemes

Peer reviewe

TOPPool: Time-aware Optimized Privacy-Preserving Ridesharing

Ridesharing is revolutionizing the transportation industry in many countries. Yet, the state of the art is based on heavily centralized services and platforms, where the service providers have full possession of the users’ location data. Recently, researchers have started addressing the challenge of enabling privacy-preserving ridesharing. The initial proposals, however, have shortcomings, as some rely on a central party, some incur high performance penalties, and most do not consider time preferences for ridesharing. TOPPool encompasses ridesharing based on the proximity of end-points of a ride as well as partial itinerary overlaps. To achieve the latter, we propose a simple yet powerful reduction to a private set intersection on trips represented as sets of consecutive road segments. We show that TOPPool includes time preferences while preserving privacy and without relying on a third party. We evaluate our approach on real-world data from the New York’s Taxi & Limousine Commission. Our experiments demonstrate that TOPPool is superior in performance over the prior work: our intersection-based itinerary matching runs in less than 0.3 seconds for reasonable trip length, in contrast, on the same set of trips prior work takes up to 10 hours

Context and Semantic Aware Location Privacy

With ever-increasing computational power, and improved sensing and communication capabilities, smart devices have altered and enhanced the way we process, perceive and interact with information. Personal and contextual data is tracked and stored extensively on these devices and, oftentimes, ubiquitously sent to online service providers. This routine is proving to be quite privacy-invasive, since these service providers mine the data they collect in order to infer more and more personal information about users. Protecting privacy in the rise of mobile applications is a critical challenge. The continuous tracking of users with location- and time-stamps expose their private lives at an alarming level. Location traces can be used to infer intimate aspects of users' lives such as interests, political orientation, religious beliefs, and even more. Traditional approaches to protecting privacy fail to meet users' expectations due to simplistic adversary models and the lack of a multi-dimensional awareness. In this thesis, the development of privacy-protection approaches is pushed further by (i) adapting to concrete adversary capabilities and (ii) investigating the threat of strong adversaries that exploit location semantics. We first study user mobility and spatio-temporal correlations in continuous disclosure scenarios (e.g., sensing applications), where the more frequently a user discloses her location, the more difficult it becomes to protect. To counter this threat, we develop adversary- and mobility-aware privacy protection mechanisms that aim to minimize an adversary's exploitation of user mobility. We demonstrate that a privacy protection mechanism must actively evaluate privacy risks in order to adapt its protection parameters. We further develop an Android library that provides on-device location privacy evaluation and enables any location-based application to support privacy-preserving services. We also implement an adversary-aware protection mechanism in this library with semantic-based privacy settings. Furthermore, we study the effects of an adversary that exploits location semantics in order to strengthen his attacks on user traces. Such extensive information is available to an adversary via maps of points of interest, but also from users themselves. Typically, users of online social networks want to announce their whereabouts to their circles. They do so mostly, if not always, by sharing the type of their location along with the geographical coordinates. We formalize this setting and by using Bayesian inference show that if location semantics of traces is disclosed, users' privacy levels drop considerably. Moreover, we study the time-of-day information and its relation to location semantics. We reveal that an adversary can breach privacy further by exploiting time-dependency of semantics. We implement and evaluate a sensitivity-aware protection mechanism in this setting as well. The battle for privacy requires social awareness and will to win. However, the slow progress on the front of law and regulations pushes the need for technological solutions. This thesis concludes that we have a long way to cover in order to establish privacy-enhancing technologies in our age of information. Our findings opens up new venues for a more expeditious understanding of privacy risks and thus their prevention