TITLE

Small Private Key MQPKS on an Embedded Microprocessor

AUTHOR(S)
Hwajeong Seo; Jihyun Kim; Jongseok Choi; Taehwan Park; Zhe Liu; Howon Kim
PUB. DATE
March 2014
SOURCE
Sensors (14248220);Mar2014, Vol. 14 Issue 3, p5441
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Multivariate quadratic (MQ) cryptography requires the use of long public and private keys to ensure a sufficient security level, but this is not favorable to embedded systems, which have limited system resources. Recently, various approaches to MQ cryptography using reduced public keys have been studied. As a result of this, at CHES2011 (Cryptographic Hardware and Embedded Systems, 2011), a small public key MQ scheme, was proposed, and its feasible implementation on an embedded microprocessor was reported at CHES2012. However, the implementation of a small private key MQ scheme was not reported. For efficient implementation, random number generators can contribute to reduce the key size, but the cost of using a random number generator is much more complex than computing MQ on modern microprocessors. Therefore, no feasible results have been reported on embedded microprocessors. In this paper, we propose a feasible implementation on embedded microprocessors for a small private key MQ scheme using a pseudo-random number generator and hash function based on a block-cipher exploiting a hardware Advanced Encryption Standard (AES) accelerator. To speed up the performance, we apply various implementation methods, including parallel computation, on-the-fly computation, optimized logarithm representation, vinegar monomials and assembly programming. The proposed method reduces the private key size by about 99.9% and boosts signature generation and verification by 5.78% and 12.19% than previous results in CHES2012.
ACCESSION #
95106774

 

Related Articles

  • Random Number Generators in Secure Disk Drives. Hars, Laszlo // EURASIP Journal on Embedded Systems;1/1/2009, Special section p1 

    Cryptographic random number generators seeded by physical entropy sources are employed in many embedded security systems, including self-encrypting disk drives, being manufactured by the millions every year. Random numbers are used for generating encryption keys and for facilitating secure...

  • Multi Carrier Steg against Omni Attacks.  // International Journal of Computer Applications;Aug2010, Vol. 5, p35 

    The article focuses on a study which investigates the efficiency of a proposed novel steganographic methodology in overcoming drawbacks of data hiding through a modern multiplexing technology. Orthogonal Frequency Division Multiplexing (OFDM) is utilized to embed data. The result of the study,...

  • Guest Editorial. Joshi, Prashant; Violante, Massimo // Journal of Electronic Testing;Jun2013, Vol. 29 Issue 3, p259 

    The article discusses various papers published within the issue, including one on a process variation-aware statistical analysis framework for aging sensors insertion, another on the impact of performance faults in modern microprocessors and a paper on a fault analysis and evaluation of truer...

  • Fault Analysis and Evaluation of a True Random Number Generator Embedded in a Processor. Soucarros, Mathilde; Clédière, Jessy; Dumas, Cécile; Elbaz-Vincent, Philippe // Journal of Electronic Testing;Jun2013, Vol. 29 Issue 3, p367 

    True Random Number Generators have many uses, in particular they play a key role in security applications and cryptographic algorithms. Our interest lies in the quality of their generated random numbers. More specifically, for such utilizations, a slight deviation of the numbers from a 'perfect'...

  • An FPGA Implementation of a Parallelized MT19937 Uniform Random Number Generator. Sriram, Vinay; Kearney, David // EURASIP Journal on Embedded Systems;1/1/2009, Special section p1 

    Recent times have witnessed an increase in use of high-performance reconfigurable computing for accelerating large-scale simulations. A characteristic of such simulations, like infrared (IR) scene simulation, is the use of large quantities of uncorrelated random numbers. It is therefore of...

  • A Novel Pseudo Random Number Generator Based on Two Plasmonic Maps. François, Michael; Grosges, Thomas; Barchiesi, Dominique; Erra, Robert // Applied Mathematics;Nov2012, Vol. 3 Issue 11, p1664 

    In plasmonic systems, the response of nanoobjects under light illumination can produce complex optical maps. Such plasmonic or resonant systems have interesting characteristics such as sensitivity on parameters and initial conditions. In this paper, we show how these complex maps can be...

  • Measuring the Insertion Attack Effect on Randomness Property of AES-based Pseudorandom Generator. Indarjani, Santi; Widjaja, Belawati // International Proceedings of Computer Science & Information Tech;2012, Vol. 40, p118 

    Random (pseudorandom) number generator (RNG/PRNG) as the heart of a cryptographic system could be a potential target for adversary to defect the security. The attack can be performed actively through insertion attack on the random outputs to reduce or even omit the randomness property. In this...

  • Generating random numbers. Holtzman, Jeff // Electronics Now;Sep98, Vol. 69 Issue 9, p22 

    Explains how to develop a random-number generator. Hardware solutions; Concept of random number; Basic formula for Lehmer Generator in C code; QBasic random-number generator.

  • Generating pseudo-random numbers. Brodie, Keith J. // Electronic Design;2/20/95, Vol. 43 Issue 4, p146 

    Presents an algorithm that uses the Linear Congruential Method (LCM) to generate the pseudo-random sequence. Equation for the sequence of randoms; Sequence length; Initialization function.

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics