Design and implementation of robust embedded processor for cryptographic applications

Practical implementations of cryptographic algorithms are vulnerable to side-channel analysis and fault attacks. Thus, some masking and fault detection algorithms must be incorporated into these implementations. These additions further increase the complexity of the cryptographic devices which already need to perform computationally-intensive operations. Therefore, the general-purpose processors are usually supported by coprocessors/hardware accelerators to protect as well as to accelerate cryptographic applications. Using a configurable processor is just another solution. This work designs and implements robust execution units as an extension to a configurable processor, which detect the data faults (adversarial or otherwise) while performing the arithmetic operations. Assuming a capable adversary who can injects faults to the cryptographic computation with high precision, a nonlinear error detection code with high error detection capability is used. The designed units are tightly integrated to the datapath of the configurable processor using its tool chain. For different configurations, we report the increase in the space and time complexities of the configurable processor. Also, we present performance evaluations of the software implementations using the robust execution units. Implementation results show that it is feasible to implement robust arithmetic units with relatively low overhead in an embedded processor

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system

In this paper, we propose a new biometric verification and template protection system which we call the THRIVE system. The system includes novel enrollment and authentication protocols based on threshold homomorphic cryptosystem where the private key is shared between a user and the verifier. In the THRIVE system, only encrypted binary biometric templates are stored in the database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during the authentication stage. The THRIVE system is designed for the malicious model where the cheating party may arbitrarily deviate from the protocol specification. Since threshold homomorphic encryption scheme is used, a malicious database owner cannot perform decryption on encrypted templates of the users in the database. Therefore, security of the THRIVE system is enhanced using a two-factor authentication scheme involving the user's private key and the biometric data. We prove security and privacy preservation capability of the proposed system in the simulation-based model with no assumption. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form but she needs to proof her physical presence by using biometrics. The system can be used with any biometric modality and biometric feature extraction scheme whose output templates can be binarized. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biohash vectors on a desktop PC running with quad-core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real life applications

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Calcul sur architecture non fiable

Although materials could be fabricated as error-free theoretically with a huge cost for worst-case design methodologies, the circuit is still susceptible to transient faults by the effects of radiation, temperature sensitivity, and etc. On the contrary, an error-resilient design enables the manufacturing process to be relieved from the variability issue so as to save material cost. Since variability and transient upsets are worsening as emerging fabrication process and size shrink are tending intense, the requirement of robust design is imminent. This thesis addresses the issue of designing on unreliable circuit. The main contributions are fourfold. Firstly a fast error-correction and low cost redundancy fault-tolerant method is presented. Moreover, we introduce judicious two-dimensional criteria to estimate the reliability and the hardware efficiency of a circuit. A general-purpose model offers low-redundancy error-resilience for contemporary logic systems as well as future nanoeletronic architectures. At last, a decoder against internal transient faults is designed in this work.En théorie, les circuits électroniques conçus selon la méthode du pire-cas sont supposés garantir un fonctionnement sans erreur pourun coût d’implémentation élevé. Dans la pratique les circuits restent sujets aux erreurs transitoires du fait de leur sensibilité aux aléastels que la radiation et la température. En revanche, une conception prenant en compte la tolérance aux fautes permet de faire face à detels aléas comme la variabilité du processus de fabrication. De plus, les erreurs transitoires et la variabilité de fabrication s’intensifientavec l’émergence de nouveaux processus de fabrication et des circuits de dimension de plus en plus réduite. La demande d’une conceptionintégrant la tolérance aux fautes devient désormais primordiale. La présente thèse a pour objectif de cerner la problématique de laconception de circuits sur des puces peu fiables et apporte des contributions suivant quatre aspects. Dans un premier temps, nous proposonsune méthode de tolérance aux fautes, basée sur la correction d’erreurs et la redondance à faible coût. Puis, nous présentonsun critère bidimensionnel judicieux permettant d’évaluer la fiabilité et l’efficacité matérielle de circuits. Nous proposons ensuite un modèleuniversel qui apporte une tolérance avec fautes à redondance faible pour les systèmes logiques d’aujourd’hui et les architecturesnanoélectroniques de demain. Enfin, nous découvrons un décodeur tolérant aux fautes transitoires internes