Fair Blind Signatures without Random Oracles

International audienceA fair blind signature is a blind signature with revocable anonymity and unlinkability, i.e., an authority can link an issuing session to the resulting signature and trace a signature to the user who requested it. In this paper we first revisit the security model for fair blind signatures given by Hufschmitt and Traoré in 2007. We then give the first practical fair blind signature scheme with a security proof in the standard model. Our scheme satisfies a stronger variant of the Hufschmitt-Traoré model

TumbleBit: an untrusted Bitcoin-compatible anonymous payment hub

This paper presents TumbleBit, a new unidirectional unlinkable payment hub that is fully compatible with today s Bitcoin protocol. TumbleBit allows parties to make fast, anonymous, off-blockchain payments through an untrusted intermediary called the Tumbler. TumbleBits anonymity properties are similar to classic Chaumian eCash: no one, not even the Tumbler, can link a payment from its payer to its payee. Every payment made via TumbleBit is backed by bitcoins, and comes with a guarantee that Tumbler can neither violate anonymity, nor steal bitcoins, nor print money by issuing payments to itself. We prove the security of TumbleBit using the real/ideal world paradigm and the random oracle model. Security follows from the standard RSA assumption and ECDSA unforgeability. We implement TumbleBit, mix payments from 800 users and show that TumbleBits offblockchain payments can complete in seconds.https://eprint.iacr.org/2016/575.pdfPublished versio

Sequential Dimensionality Reduction for Extracting Localized Features

Linear dimensionality reduction techniques are powerful tools for image analysis as they allow the identification of important features in a data set. In particular, nonnegative matrix factorization (NMF) has become very popular as it is able to extract sparse, localized and easily interpretable features by imposing an additive combination of nonnegative basis elements. Nonnegative matrix underapproximation (NMU) is a closely related technique that has the advantage to identify features sequentially. In this paper, we propose a variant of NMU that is particularly well suited for image analysis as it incorporates the spatial information, that is, it takes into account the fact that neighboring pixels are more likely to be contained in the same features, and favors the extraction of localized features by looking for sparse basis elements. We show that our new approach competes favorably with comparable state-of-the-art techniques on synthetic, facial and hyperspectral image data sets.Comment: 24 pages, 12 figures. New numerical experiments on synthetic data sets, discussion about the convergenc

Raman Spectroscopic Analysis of Geological and Biogeological Specimens of Relevance to the ExoMars Mission

H.G.M.E., I.H., and R.I. acknowledge the support of the STFC Research Council in the UK ExoMars programme. J.J. and P.V. acknowledge the support of the Grant Agency of the Czech Republic (210/10/0467) and of the Ministry of Education of the Czech Republic (MSM0021620855).Peer reviewedPublisher PD