Zekeriya Erkin

Privacy-Preserving Face Recognition

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 Pailler-encrypted numbers. We show through extensive experiments that the system can be run efficiently on conventional hardware.

Protection and retrieval of encrypted multimedia content: when cryptography meets signal processing

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-togo 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.

Generating Private Recommendations Efficiently Using Homomorphic Encryption and Data Packing

Recommender systems have become an important tool for personalization of online services. Generating recommendations in online services depends on privacy-sensitive data collected from the users. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties, but not the service provider. This creates a serious privacy risk for the users. In this paper, we aim to protect the private data against the service provider while preserving the functionality of the system. We propose encrypting private data and processing them under encryption to generate recommendations. By introducing a semitrusted third party and using data packing, we construct a highly efficient system that does not require the active participation of the user. We also present a comparison protocol, which is the first one to the best of our knowledge, that compares multiple values that are packed in one encryption. Conducted experiments show that this work opens a door to generate private recommendations in a privacy-preserving manner.

Private computation of spatial and temporal power consumption with smart meters

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 meterings 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 peer-based nature, i.e., no need for any on-line aggregators or trusted third parties.

Privacy-preserving data aggregation in smart metering systems: an overview

Growing energy needs are forcing governments to look for alternative resources and ways to better manage the energy grid and load balancing. As a major initiative, many countries including the United Kingdom, United States, and China have already started deploying smart grids. One of the biggest advantages of smart grids compared to traditional energy grids is the ability to remotely read fine-granular measurements from each smart meter, which enables the grid operators to balance load efficiently and offer adapted time-dependent tariffs. However, collecting fine-granular data also poses a serious privacy threat for the citizens as illustrated by the decision of the Dutch Parliament in 2009 that rejects the deployment of smart meters due to privacy considerations. Hence, it is a must to enforce privacy rights without disrupting the smart grid services like billing and data aggregation. Secure signal processing (SSP) aims at protecting the sensitive data by means of encryption and provides tools to process them under encryption, effectively addressing the smart metering privacy problem.

Anonymous fingerprinting with robust QIM watermarking techniques

Fingerprinting is an essential tool to shun legal buyers of digital content from illegal redistribution. In fingerprinting schemes, the merchant embeds the buyers identity as a watermark into the content so that the merchant can retrieve the buyers identity when he encounters a redistributed copy. To prevent the merchant from dishonestly embedding the buyers identity multiple times, it is essential for the fingerprinting scheme to be anonymous. Kuribayashi and Tanaka, 2005, proposed an anonymous fingerprinting scheme based on a homomorphic additive encryption scheme, which uses basic quantization index modulation (QIM) for embedding. In order, for this scheme, to provide sufficient security to the merchant, the buyer must be unable to remove the fingerprint without significantly degrading the purchased digital content. Unfortunately, QIM watermarks can be removed by simple attacks like amplitude scaling. Furthermore, the embedding positions can be retrieved by a single buyer, allowing for a locally targeted attack. In this paper, we use robust watermarking techniques within the anonymous fingerprinting approach proposed by Kuribayashi and Tanaka. We show that the properties of an additive homomorphic cryptosystem allow for creating anonymous fingerprinting schemes based on distortion compensated QIM (DC-QIM) and rational dither modulation (RDM), improving the robustness of the embedded fingerprints. We evaluate the performance of the proposed anonymous fingerprinting schemes under additive-noise and amplitude-scaling attacks.

Privacy in Online Social Networks

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.

Privacy in Recommender Systems

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.

Efficiently computing private recommendations

Online recommender systems enable personalized service to users. The underlying collaborative filtering techniques operate on privacy sensitive user data, which could be misused by the service provider. To protect user privacy, we propose to encrypt the data and generate recommendations by processing them under encryption. Thus, the service provider observes neither user preferences nor recommendations. The proposed method uses homomorphic encryption and secure multi-party computation (MPC) techniques, which introduce a significant overhead in computational complexity. We minimize the introduced overhead by packing data and using cryptographic protocols particularly developed for this purpose. The proposed cryptographic protocol is implemented to test its correctness and performance.

Privacy-preserving user clustering in a social network

In a ubiquitously connected world, social networks are playing an important role on the Internet by allowing users to find groups of people with similar interests. The data needed to construct such networks may be considered sensitive personal information by the users, which raises privacy concerns. The problem of building social networks while user privacy is protected is hence crucial for further development of such networks. K-means clustering is widely used for clustering users in a social network. In this paper, we provide an efficient privacy-preserving variant of K-means clustering. The scenario we consider involves a server and multiple users where users need to be grouped into K clusters. In our protocol the server is not allowed to learn the individual user data and users are not allowed to learn the cluster centers. The experiments on the MovieLens dataset show that deployment of the system for real use is reasonable as its efficiency even on conventional hardware is promising.