We study a context-aware preference learning system that automatically learns user preferences in different environments. The system is based on a Dirichlet process Gaussian mixture model and comprises an environmental model and a preference learning system. The environmental model is used to determine the user’s current environment, based on environmental signals. The preference learning system actively learns user preferences through pairwise comparison trials. During these trials, the so-called reference and alternative proposals are generated by the system and presented to a user, who, in his/her turn, indicates the preferred one. We propose a novel inference approach that allows for environmental model per-sonalization and leads to improved system performance. This is achieved by introducing a two-way dependency between environment and preference domains. Our approach is validated through simulations that demonstrate a significant performance improvement with respect to the baseline model.

Physical unclonable functions (PUFs) are relatively new security primitives used for device authentication and device-specific secret key generation. In this paper we focus on SRAM- PUFs. The SRAM-PUFs enjoy uniqueness and randomness properties stemming from the intrinsic randomness of SRAM memory cells, which is a result of manufacturing variations. This randomness can be translated into the cryptographic keys thus avoiding the need to store and manage the device cryptographic keys. Therefore these properties, combined with the fact that SRAM memory can be often found in today's IoT devices, make SRAM-PUFs a promising candidate for securing and authentication of the resource-constrained IoT devices. PUF observations are always affected by noise and environmental changes. Therefore secret- generation schemes with helper data are used to guarantee reliable regeneration of the PUF-based secret keys. Error correction codes (ECCs) are an essential part of these schemes. In this work, we propose a practical error correction construction for PUF-based secret generation that are based on polar codes. The resulting scheme can generate 128-bit keys using 1024 SRAM-PUF bits and 896 helper data bits and achieve a failure probability of 10^{-9} or lower for a practical SRAM-PUFs setting with bit error probability of 15%. The method is based on successive cancellation combined with list decoding and hash-based checking that makes use of the hash that is already available at the decoder. In addition, an adaptive list decoder for polar codes is investigated. This decoder increases the list size only if needed.

Fuzzy commitment and syndrome-based schemes are two well-known helper data schemes used to bind and generate, respectively, a secret key to/from SRAM-PUF observations. To allow the decoder to reconstruct this secret key from a new (verification) observation of an SRAM-PUF, an encoder has to generate so-called helper data. This helper data is a function of an SRAM-PUF enrollment observation and, in case of fuzzy commitment, the secret key. The helper data is assumed to be public and thus must leak no information about the secret key. It is known that both schemes can achieve secrecy capacity equal to the mutual information between enrollment and verification SRAM-PUF observations at zero secrecy leakage, when the observations are unbiased and a single enrollment is performed. We study here the situation when multiple SRAM-PUF observations are used to create multiple secret keys. First, we introduce a symmetry property for multiple SRAM-PUF observations. For such symmetric SRAM-PUFs, we show that, in both helper data schemes, the helper data corresponding to multiple SRAM-PUF observations provide no information about any of the secret keys.

In this paper, we analyze two types of biometric identification systems with protected templates that also support authentication. In the first system, two terminals observe biometric enrollment and identification sequences of a number of individuals. It is the goal of these terminals to form a common secret for the sequences belonging to the same individual by interchanging public (helper) messages of all individuals such that the information leakage about the secrets from these helper messages is negligible. These secret keys are used for authentication purposes. Moreover, the second terminal should be able to establish the identity of an individual based on the presented biometric identification sequence and helper messages. It is important to realize that biometric data are unique for individuals and cannot be replaced if compromised. Therefore, the helper messages should contain as little as possible information about the biometric data. In the second setting, we consider the first terminal does not generate secret keys from biometric sequences of individuals but chooses them uniformly at random. These keys are conveyed to the second terminal by communicating the corresponding helper messages. In this paper, we determine the fundamental tradeoffs between secret-key, identification, and privacy-leakage rates for both biometric settings.

In biometric privacy-preserving authentication systems that are based on key-binding, two terminals observe two correlated biometric sequences. The first terminal selects a secret key, which is independent of the biometric data, binds this secret key to the observed biometric sequence and communicates it to the second terminal by sending a public message. This message should only contain a negligible amount of information about the secret key, but also leak as little as possible about the biometric data. Current approaches to realize such biometric systems use fuzzy commitment with codes that, given a secret-key rate, can only achieve the corresponding privacy-leakage rate equal to one minus this secret-key rate. However, the results in Willems and Ignatenko [2009] indicate that lower privacy leakage can be achieved if vector quantization is used at the encoder. In this paper we study the use of convolutional and turbo codes applied in fuzzy commitment and its modifications that realize this.

Virtual organizations are dynamic, interorganizational collaborations that involve systems and services belonging to different security domains. Several solutions have been proposed to guarantee the enforcement of the access control policies protecting the information exchanged in a distributed system, but none of them addresses the dynamicity characterizing virtual organizations. In this paper we propose a dynamic hierarchical attribute-based encryption (D-HABE)scheme that allows the institutions in a virtual organization to encrypt information according to a policy in such a way that only users with appropriate attributes can decrypt it. In addition, we introduce a key management scheme that determines which user is entitled to receive which attribute key from which domain authority.

We present models for authentication in biometric settings. We could determine the trade-off between false-acceptance exponents and the so-called privacy-leakage rate. In this way we extend the Ahlswede-Csiszar secret-key generation result [1993] and more specifically the secret-key rate vs. privacy-leakage rate trade-off, studied by Ignatenko and Willems [2009].

In this paper we analyze secret generation in biometric identification systems with protected templates. This problem is closely related to the study of the biometric identification capacity of Willems et al. 2003 and O'Sullivan and Schmid 2002 and the common randomness generation of Ahlswede and Csiszár 1993. In our system two terminals observe biometric enrollment and identification sequences of a number of individuals. It is the goal of these terminals to form a common secret for the sequences that belong to the same individual by interchanging public (helper) messages for all individuals in such a way that the information leakage about the secrets from these helper messages is negligible. It is important to realize that biometric data are unique for individuals and cannot be replaced if compromised. Therefore the helper messages should contain as little as possible information about the biometric data. On the other hand, the second terminal has to establish the identity of the individual who presented his biometric sequence, based on the helper data produced by the first terminal. In this paper we determine the fundamental tradeoff between secret-key rate, identification rate and privacy-leakage rate in biometric identification systems.

In 1999, Juels and Wattenberg introduced the fuzzy commitment scheme. This scheme is a particular realization of a binary biometric secrecy system with chosen secret keys. It became a popular technique for designing biometric secrecy systems, since it is convenient and easy to implement using standard error-correcting codes. This paper investigates privacy- and secrecy-leakage in fuzzy commitment schemes. The analysis is carried out for four cases of biometric data statistics, i.e., memoryless totally symmetric, memoryless input-symmetric, memoryless, and stationary ergodic. First, the achievable regions are determined for the cases when data statistics are memoryless totally symmetric and memoryless input-symmetric. For the general memoryless and stationary ergodic cases, only outer bounds for the achievable rate-leakage regions are provided. These bounds, however, are sharpened for systematic parity-check codes. Given the achievable regions (bounds), the optimality of fuzzy commitment is assessed. The analysis shows that fuzzy commitment is only optimal for the memoryless totally symmetric case if the scheme operates at the maximum secret-key rate. Moreover, it is demonstrated that for the general memoryless and stationary ergodic cases, the scheme leaks information on both the secret and biometric data.

Ahlswede and Csiszar [1993] introduced the concept of secret sharing. In their source model two terminals observe two correlated sequences. It is the objective of the terminals to form a common secret by interchanging a public message (helper data) in such a way that the secrecy leakage is negligible. In a biometric setting, where the sequences correspond to the enrollment and authentication data, respectively, it is crucial that the public message leaks as little information as possible about the biometric data, since compromised biometric data cannot be replaced. We investigated the fundamental trade-offs for four biometric settings. The first one is the standard (Ahlswede-Csiszar) secret generation setting, for which we determined the secret-key vs. privacy-leakage rate region. Here leakage corresponds to the mutual information between helper data and biometric enrollment sequence. In the second setting the secret is not generated by the terminals but independently chosen, and transmitted using a public message. Again we determined the region of achievable rate-leakage pairs. In setting three and four we consider zero-leakage, i.e. the public message contains only a negligible amount of information about the secret and about the biometric enrollment sequence. To achieve this a private key is needed, which can be observed only by the terminals. We considered again both secret generation and secret transmission and determined for both cases the region of achievable secret-key vs. private-key rate pairs.

This paper addresses privacy leakage in biometric secrecy systems. Four settings are investigated. The first one is the standard Ahlswede-Csiszar secret-generation setting in which two terminals observe two correlated sequences. They form a common secret by interchanging a public message. This message should only contain a negligible amount of information about the secret, but here, in addition, we require it to leak as little information as possible about the biometric data. For this first case, the fundamental tradeoff between secret-key and privacy-leakage rates is determined. Also for the second setting, in which the secret is not generated but independently chosen, the fundamental secret-key versus privacy-leakage rate balance is found. Settings three and four focus on zero-leakage systems. Here the public message should only contain a negligible amount of information on both the secret and the biometric sequence. To achieve this, a private key is needed, which can only be observed by the terminals. For both the generated-secret and the chosen-secret model, the regions of achievable secret-key versus private-key rate pairs are determined. For all four settings, the fundamental balance is determined for both unconditional and conditional privacy leakage.

We focus here on two secure biometric systems (a common randomness based scheme and a fuzzy commitment scheme) and discuss their privacy preserving properties. We derive bounds on the privacy leakage in these schemes. We also show the relation between employed error-correction and leakage on biometric information, and between privacy and security for the fuzzy commitment scheme.

We propose methods to estimate the secrecy-rate of fuzzy sources (e.g. biometrics and physical unclonable functions (PUFs)) using context-tree weighting. In this paper we focus on PUFs. In order to show that our estimates are realistic we first generalize Maurer's (1993) result to the ergodic case. Then we focus on the fact that the entropy of a stationary two-dimensional structure is a limit of a series of conditional entropies, a result by Anastassiou and Sakrison (1982). We extend this result to the conditional entropy of one two-dimensional structure given another one. Finally we show that the general CTW-method approaches the source entropy also in the two-dimensional stationary case. We further extend this result to the two-dimensional conditional entropy. Based on the obtained results we do several measurements on (our) optical PUFs. These measurements allow us to conclude that a secrecy-rate of 0.3 bit/location is possible