A Collaborative Protocol for Private Retrieval of Location-Based Information

Privacy and security are paramount for the proper deployment of location-based services (LBSs). We present a novel protocol based on user collaboration to privately retrieve location-based information from an LBS provider. Our approach neither assumes that users or the LBS can be completely trusted with regard to privacy, nor relies on a trusted third party. In addition, user queries, containing accurate locations, remain unchanged, and the collaborative protocol does not impose any special requirements on the query-response function of the LBS. The protocol is analyzed in terms of privacy, network traffic, and LBS processing overhead. We show that our proposal provides exponential scalability in the probability of guaranteed privacy breach, at the expense of a linear relative network cost.Preprin

The Long Road to Computational Location Privacy: A Survey

The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed

PRIVACY PRESERVATION IN LOCATION-BASED PROXIMITY SERVICES

One of the most common location-based services (LBS) in the geo-aware social network market is the notification of friends geographically in proximity. In addition to the privacy threats related to the use of traditional LBS, there are other privacy threats specific to proximity services. Existing privacy-preserving solutions for LBS are not effective or directly applicable. For this reason, we developed techniques that specifically address the privacy threats of this type of services. The proposed techniques let a user control what is disclosed about her location and formally guarantee that these requirements are satisfied. An extensive empirical evaluation was performed, by using a dataset of user movement generated using an agent-based simulator, in which agents reflect the behavior of typical users of proximity services. The techniques were also integrated in a fully functional privacy-aware proximity service, for which we developed desktop and mobile clients

Location Privacy for Mobile Crowd Sensing through Population Mapping

Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users\u27 mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users\u27 privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces

Location Estimation Methods for Open, Privacy Preserving Mobile Positioning

The future is mobile and location aware. More and more of our gadgets are portable and have an online presence. For our location-aware mobile future to be safe, we need to demand that our privacy and anonymity be protected. Currently, each and every location aware-system or feature requires us to give new people, corporations and entities access to one of our most intimate attributes, our location. The main solutions to ameliorate this have been by cloaking or hiding users from service providers or by moving trust to other "more trustable" parties. We want to minimize the need for trust. Your location is your own, and you should not have to pay with your privacy to determine it. Our focus lies on location estimation services - services that calculate your location based on measurements done on your network equipment - as they are the main drive behind the location-aware future. You can freely choose, discriminate against, and cloak yourself from services asking for your location, whereas removing the ability to determine your own location effectively impedes location awareness. We are interested in producing a freely available, open source, privacy preserving, community sourced, and safe location estimation service that minimizes the need for trust. In this thesis we focus on three things: Designing such a system, testing different ways of estimating locations, and determining the best way of estimating locations for the designed system