Web queries, credit card transactions, and medical records are examples of transaction data flowing in corporate data stores, and often revealing associations between individuals and sensitive information. The serial release of these data to partner institutions or data analysis centers in a nonaggregated form is a common situation. In this paper, we show that correlations among sensitive values associated to the same individuals in different releases can be easily used to violate users' privacy by adversaries observing multiple data releases, even if state-of-the-art privacy protection techniques are applied. We show how the above sequential background knowledge can be actually obtained by an adversary, and used to identify with high confidence the sensitive values of an individual. Our proposed defense algorithm is based on Jensen-Shannon divergence; experiments show its superiority with respect to other applicable solutions. To the best of our knowledge, this is the first work that systematically investigates the role of sequential background knowledge in serial release of transaction data.

The retrieval of close-by points of interest (POIs) is becoming a popular location-based service (LBS), often integrated with navigational services and geo-social networks. However, the access to POI services is prone to potentially serious privacy issues, since requests for POIs often include sensitive information like the user's location and her personal interests. Many techniques to enforce privacy in LBS have been proposed in the literature, in some cases focusing on anonymizing the requests and in others on obfuscating information in order to decrease its sensitivity. In many cases privacy protection comes at some cost in terms of service precision and performance. In this paper we propose a novel technique that combines the above cited approaches, overcomes some of their limitations in terms of assumptions on adversary knowledge, while still guaranteeing service precision. Our privacy solution has been integrated in an existing distributed system to share and retrieve POIs based not only on the user's current location but also on other (possibly sensitive) context data.

The anonymization of location based queries through the generalization of spatio-temporal information has been proposed as a privacy preserving technique. We show that the presence of multiple concurrent requests, the repetition of similar requests by the same issuers, and the distribution of different service parameters in the requests can significantly affect the level of privacy obtained by current anonymity-based techniques. We provide a formal model of the privacy threat, and we propose an incremental defense technique based on a combination of anonymity and obfuscation. We show the effectiveness of this technique by means of an extensive experimental evaluation.

This paper presents preliminary results on anonymization and obfuscation techniques to preserve users' privacy in context-aware service provisioning. The techniques are based on generalizing request parameters as well as the context data provided to the application. Local context semantic aggregation is used to improve the quality of service that can be achieved while preserving privacy. The paper also shows how the software architecture of the CARE middleware can be extended to implement the proposed techniques.

The large scale adoption of adaptive services in pervasive and mobile computing is likely to be conditioned to the availability of reliable privacy-preserving technologies. Unfortunately, the research in this field can still be considered in its infancy. This paper considers a specific pervasive computing scenario, and shows that the application of state-of-the-art techniques for the anonymization of service requests is insufficient to protect the privacy of users. A specific class of attacks, called shadow attacks, is formally defined and a set of defense techniques is proposed. These techniques are validated through the use of a simulator and an extensive set of experiments.