Sender anonymity in location-based services (LBS) attempts to hide the identity of a mobile device user who sends requests to the LBS provider for services in her proximity (e.g. ¿find the nearest gas station¿ etc.). The goal is to keep the requester's interests private even from attackers who (via hacking or subpoenas) gain access to the request and to the locations of the mobile user and other nearby users at the time of the request. In an LBS context, the best-studied privacy guarantee is known as sender k-anonymity. We show that state-of-the art solutions for sender k-anonymity defend only against naive attackers who have no knowledge of the anonymization policy that is in use. We strengthen the privacy guarantee to defend against more realistic ¿policy-aware¿ attackers. We describe a polynomial algorithm to obtain an optimum anonymization policy. Our implementation and experiments show that the policy-aware sender k-anonymity has potential for practical impact, being efficiently enforceable, with limited reduction in utility when compared to policy-unaware guarantees.

The web and converged services paradigm promises tremendous flexibility in the creation of rich composite services for enterprises and end users. The flexibility and richness offers the possibility of highly customized, individualized services for the end user and hence revenue generating services for service providers (e.g. ASPs, telecom network operators, ISPs). But how can end users (and enterprises) specify their preferences when a myriad of possibilities and potential circumstances need to be addressed? In this paper, we advocate a solution based on policy management where user preferences are specified through forms but translated into rules in a high-level policy language. This paper identifies the requirements for this kind of interpretation, and describes the Houdini system (developed at Bell Labs) which offers a rich rule-based language and a framework that supports intuitive, forms-based provisioning interfaces.

This paper provides detail on two key components of the Houdini framework under development at Bell Labs, that enable context-aware and privacy-conscious user data sharing appropriate for mobile and/or ubiquitous computing. The framework includes an approach for integrating data from diverse sources, for gathering user preferences for what data to share and when to share it, and a policy management infrastructure in the network for enforcing those preferences. The current paper focuses on two components of this infrastructure that are essential for mobile and ubiquitous computing, namely the framework to support self-provisioning of preferences, and the performance of the underlying rules engine.