On the concatenated structures of a [49, 18, 12] binary abelian code

AbstractWe here introduce a new formalism for describing concatenated codes. Using this formalism, we show how any generalized concatenated code can be viewed as a first order concatenated code. Finally, we give an illustrative example: using Jensen's result (1985) which shows that any abelian code has a generalized concatenated structure, we first give the representation of the [49, 18, 12] abelian code introduced by Camion (1971) as a second order concatenated code; then using our description, we show that this code is also equal to the first order concatenation of two linear cyclic codes

The best of both worlds: Applying secure sketches to cancelable biometrics

AbstractCancelable biometrics and secure sketches have been introduced with the same purpose in mind: to protect the privacy of biometric templates while keeping the ability to match this protected data against a reference. The paradigm beyond cancelable biometrics is to perform an irreversible transformation over images and to make matching over transformed images. On one hand, a drawback of this technique is that for biometrics using a matching algorithm relying on some complex characteristics, such as the ones used for fingerprints, the irreversible transformation tends to break the underlying structure, thus degrading the performance accuracy. On the other hand, for secure sketches, matching is reduced to an error correction and we show here that applying secure sketch error correction to cancelable biometrics allows one to keep good matching performance. Moreover, the security’s advantages of both schemes adds up together

Analysis of Biometric Authentication Protocols in the Blackbox Model

In this paper we analyze different biometric authentication protocols considering an internal adversary. Our contribution takes place at two levels. On the one hand, we introduce a new comprehensive framework that encompasses the various schemes we want to look at. On the other hand, we exhibit actual attacks on recent schemes such as those introduced at ACISP 2007, ACISP 2008, and SPIE 2010, and some others. We follow a blackbox approach in which we consider components that perform operations on the biometric data they contain and where only the input/output behavior of these components is analyzed.Comment: 10 pages, 1 figures, submitted to IEEE Transactions on Information Forensics and Securit

A further improvement of the work factor in an attempt at breaking McEliece's cryptosystem

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On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent Gaussian random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this paper, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201

On the key equation for n-dimensional cyclic codes. Applications to decoding

We introduce the key equation of a multidimensional code. This equation exhibits the error-locator polynomial as product of univariate polynomials and the error-evaluator polynomial as a multivariate polynomial. Then we reinterpret these polynomials in a multidimensional linear recurring sequence context. In particular, using the concept of section, we reduce the solution of the decoding problem to a succession of application of the Berlekamp-Massey algorithm. However, it must be noted that multidimensional codes which are usefull for applications and which are decodable by our algorithm are left to be found

Funshade: Functional Secret Sharing for Two-Party Secure Thresholded Distance Evaluation

We propose a novel privacy-preserving, two-party computation of various distance metrics (e.g., Hamming distance, Scalar Product) followed by a comparison with a fixed threshold, which is known as one of the most useful and popular building blocks for many different applications including machine learning, biometric matching, etc. Our solution builds upon recent advances in functional secret sharing and makes use of an optimized version of arithmetic secret sharing. Thanks to this combination, our new solution named Funshade is the first to require only one round of communication and two ring elements of communication in the online phase, outperforming all prior state-of-the-art schemes while relying on lightweight cryptographic primitives. Lastly, we implement the solution from scratch in Python using efficient C++ blocks, testifying its high performance