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.

The practical application of Secure Two-Party Computation is hindered by the difficulty to implement secure computation protocols. While recent work has proposed very simple programming languages which can be used to specify secure computations, it is still difficult for practitioners to use them, and cumbersome to translate existing source code into this format. Similarly, the manual construction of two-party computation protocols, in particular ones based on the approach of garbled circuits, is labor intensive and error-prone.The central contribution of the current paper is a tool which achieves Secure Two-Party Computation for ANSI C. Our work is based on a combination of model checking techniques and two-party computation based on garbled circuits.Our key insight is a nonstandard use of the bit-precise model checker CBMC which enables us to translate C programs into equivalent Boolean circuits. To this end, we modify the standard CBMC translation from programs into Boolean formulas whose variables correspond to the memory bits manipulated by the program. As CBMC attempts to minimize the size of the formulas, the circuits obtained by our tool chain are also size efficient; to improve the efficiency of the garbled circuit evaluation, we perform optimizations on the circuits. Experimental results with the new tool CBMC-GC demonstrate the practical usefulness of our approach.

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In this tutorial paper we present one of the simplest autonomous differential equations capable of generating chaotic behavior. Some of the fundamental routes to chaos and bifurcation phenomena are demonstrated with examples. A brief discussion of equilibrium points and their stability is given. For the convenience of the reader, a short computer program written in QuickBASIC is included to give the reader a possibility of quick hands-on experience with the generation of chaotic phenomena without using sophisticated numerical simulators. All the necessary parameter values and initial conditions are provided in a tabular form. Eigenvalue diagrams showing regions with particular eigenvalue patterns are given.

Abstract-Much is known about the design of automated systems to search broadcast news, but it has only recently become possible to apply similar techniques to large collections of spontaneous speech. This paper presents initial results from experiments with speech recognition, topic segmentation, topic categorization, and named entity detection using a large collection of recorded oral histories. The work leverages a massive manual annotation effort on 10 000 h of spontaneous speech to evaluate the degree to which automatic speech recognition (ASR)-based segmentation and categorization techniques can be adapted to approximate decisions made by human annotators. ASR word error rates near 40% were achieved for both English and Czech for heavily accented, emotional and elderly spontaneous speech based on 65-84 h of transcribed speech. Topical segmentation based on shifts in the recognized English vocabulary resulted in 80% agreement with manually annotated boundary positions at a 0.35 false alarm rate. Categorization was considerably more challenging, with a nearestneighbor technique yielding F = 0 3. This is less than half the value obtained by the same technique on a standard newswire categorization benchmark, but replication on human-transcribed interviews showed that ASR errors explain little of that difference. The paper concludes with a description of how these capabilities Manuscript

Secure two-party computation (STC) is a computer security paradigm where two parties can jointly evaluate a program with sensitive input data, provided in parts from both parties. By the security guarantees of STC, neither party can learn any information on the other party's input while performing the STC task. For a long time thought to be impractical, until recently, STC has only been implemented with domain-specific languages or hand-crafted Boolean circuits for specific computations. Our open-source compiler CBMC-GC is the first ANSI C compiler for STC. It turns C programs into Boolean circuits that fit the requirements of garbled circuits, a generic STC approach based on circuits. Here, the size of the resulting circuits plays a crucial role since each STC step involves encryption and network transfer and is therefore extremely slow when compared to computations performed on modern hardware architectures. We report on newly implemented circuit optimization techniques that substantially reduce the circuit sizes compared to the original release of CBMC-GC.

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