Abstract. Face recognition is increasingly deployed as a means to unobtrusively verify the identity of people. The widespread use of biometrics raises important privacy concerns, in particular if the biometric matching process is performed at a central or untrusted server, and calls for the implementation of Privacy-Enhancing Technologies. In this paper we propose for the first time a strongly privacy-enhanced face recognition system, which allows to efficiently hide both the biometrics and the result from the server that performs the matching operation, by using techniques from secure multiparty computation. We consider a scenario where one party provides a face image, while another party has access to a database of facial templates. Our protocol allows to jointly run the standard Eigenfaces recognition algorithm in such a way that the first party cannot learn from the execution of the protocol more than basic parameters of the database, while the second party does not learn the input image or the result of the recognition process. At the core of our protocol lies an efficient protocol for securely comparing two Paillerencrypted numbers. We show through extensive experiments that the system can be run efficiently on conventional hardware.

Abstract. Smart metering of utility consumption is rapidly becoming reality for multitudes of people and households. It promises real-time measurement and adjustment of power demand which is expected to result in lower overall energy use and better load balancing. On the other hand, finely granular measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including all kinds of personal attributes and activities. Reconciling smart metering's benefits with privacy concerns is a major challenge.In this paper we explore some simple and relatively efficient cryptographic privacy techniques that allow spatial (group-wide) aggregation of smart meter measurements. We also consider temporal aggregation of multiple measurements for a single smart meter. While our work is certainly not the first to tackle this topic, we believe that proposed techniques are appealing due to their simplicity, few assumptions and peerbased nature, i.e., no need for any on-line aggregators or trusted third parties.

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Online Social Networks (OSNs) have become part of daily life for millions of users. Users building explicit networks that represent their social relationships and often share a wealth of personal information to their own benefit. The potential privacy risks of such behavior are often underestimated or ignored. The problem is exacerbated by lacking experience and awareness in users, as well as poorly designed tools for privacy-management on the part of the OSN. Furthermore, the centralized nature of OSNs makes users dependent and puts the Service Provider in a position of power. Because Service Providers are not by definition trusted or trustworthy, their practices need to be taken into account when considering privacy risks. This chapter aims to provide insight into privacy in OSNs. First, a classification of different types of OSNs based on their nature and purpose is made. Next, different types of data contained in OSNs are distinguished. The associated privacy risks in relation to both users and Service Providers are identified, and finally relevant research areas for privacy-protecting techniques are discussed. Clear mappings are made to reflect typical relations that exist between OSN type, data type, particular privacy risks and privacy-preserving solutions.

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The processing and encryption of multimedia content are generally considered sequential and independent operations. In certain multimedia content processing scenarios, it is, however, desirable to carry out processing directly on encrypted signals. The field of secure signal processing poses significant challenges for both signal processing and cryptography research; only few ready-to-go fully integrated solutions are available. This study first concisely summarizes cryptographic primitives used in existing solutions to processing of encrypted signals, and discusses implications of the security requirements on these solutions. The study then continues to describe two domains in which secure signal processing has been taken up as a challenge, namely, analysis and retrieval of multimedia content, as well as multimedia content protection. In each domain, state-of-the-art algorithms are described. Finally, the study discusses the challenges and open issues in the field of secure signal processing.

In many online applications, the range of content that is offered to users is so wide that a need for automated recommender systems arises. Such systems can provide a personalized selection of relevant items to users. In practice, this can help people find entertaining movies, boost sales through targeted advertisements, or help social network users meet new friends.To generate accurate personalized recommendations, recommender systems rely on detailed personal data on the preferences of users. Examples are ratings, consumption histories, and personal profiles. Recommender systems are useful, however the privacy risks associated to gathering and processing personal data are often underestimated or ignored. Many users are not sufficiently aware if and how much of their data is collected, if such data is sold to third parties, or how securely it is stored and for how long.This chapter aims to provide insight into privacy in recommender systems. First, we discuss different types of existing recommender systems. Second, we give an overview of the data that is used in recommender systems. Third, we examine the associated risks to data privacy. Fourth, relevant research areas for privacy-protection techniques and their applicability to recommender systems are discussed. Finally, we conclude with a discussion on applying and combining different privacy-protection techniques in real-world settings, making clear mappings to reflect typical relations between recommender system types, information types, particular privacy risks, and privacy-protection techniques.