On the factorization of polynomials over algebraic fields

SIGLEAvailable from British Library Document Supply Centre- DSC:DX86869 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Is There an Oblivious RAM Lower Bound?

An Oblivious RAM (ORAM), introduced by Goldreich and Ostrovsky (JACM 1996), is a (probabilistic) RAM that hides its access pattern, i.e. for every input the observed locations accessed are similarly distributed. Great progress has been made in recent years in minimizing the overhead of ORAM constructions, with the goal of obtaining the smallest overhead possible. We revisit the lower bound on the overhead required to obliviously simulate programs, due to Goldreich and Ostrovsky. While the lower bound is fairly general, including the offline case, when the simulator is given the reads and writes ahead of time, it does assume that the simulator behaves in a “balls and bins” fashion. That is, the simulator must act by shuffling data items around, and is not allowed to have sophisticated encoding of the data. We prove that for the offline case, showing a lower bound without the above restriction is related to the size of the circuits for sorting. Our proof is constructive, and uses a bit-slicing approach which manipulates the bit representations of data in the simulation. This implies that without obtaining yet unknown superlinear lower bounds on the size of such circuits, we cannot hope to get lower bounds on offline (unrestricted) ORAMs

Modern Computer Arithmetic (version 0.5.1)

This is a draft of a book about algorithms for performing arithmetic, and their implementation on modern computers. We are concerned with software more than hardware - we do not cover computer architecture or the design of computer hardware. Instead we focus on algorithms for efficiently performing arithmetic operations such as addition, multiplication and division, and their connections to topics such as modular arithmetic, greatest common divisors, the Fast Fourier Transform (FFT), and the computation of elementary and special functions. The algorithms that we present are mainly intended for arbitrary-precision arithmetic. They are not limited by the computer word size, only by the memory and time available for the computation. We consider both integer and real (floating-point) computations. The book is divided into four main chapters, plus an appendix. Our aim is to present the latest developments in a concise manner. At the same time, we provide a self-contained introduction for the reader who is not an expert in the field, and exercises at the end of each chapter. Chapter titles are: 1, Integer Arithmetic; 2, Modular Arithmetic and the FFT; 3, Floating-Point Arithmetic; 4, Elementary and Special Function Evaluation; 5 (Appendix), Implementations and Pointers. The book also contains a bibliography of 236 entries, index, summary of notation, and summary of complexities.Comment: Preliminary version of a book to be published by Cambridge University Press. xvi+247 pages. Cite as "Modern Computer Arithmetic, Version 0.5.1, 5 March 2010". For further details, updates and errata see http://wwwmaths.anu.edu.au/~brent/pub/pub226.html or http://www.loria.fr/~zimmerma/mca/pub226.htm

Proceedings of SAT Competition 2020 : Solver and Benchmark Descriptions

Non peer reviewe

Spectral modular arithmetic

In many areas of engineering and applied mathematics, spectral methods provide very powerful tools for solving and analyzing problems. For instance, large to extremely large sizes of numbers can efficiently be multiplied by using discrete Fourier transform and convolution property. Such computations are needed when computing π to millions of digits of precision, factoring and also big prime search projects. When it comes to the utilization of spectral techniques for modular operations in public key cryptosystems two difficulties arise; the first one is the reduction needed after the multiplication step and the second is the cryptographic sizes which are much shorter than the optimal asymptotic crossovers of spectral methods. In this dissertation, a new modular reduction technique is proposed. Moreover, modular multiplication is given based on this reduction. These methods work fully in the frequency domain with some exceptions such as the initial, final and partial transformations steps. Fortunately, the new technique addresses the reduction problem however, because of the extra complexity coming from the overhead of the forward and backward transformation computations, the second goal is not easily achieved when single operations such as modular multiplication or reduction are considered. On the contrary, if operations that need several modular multiplications with respect to the same modulus are considered, this goal is more tractable. An obvious example of such an operation is the modular exponentiation i.e., the computation of c=m[superscript e] mod n where c, m, e, n are large integers. Therefore following the spectral modular multiplication operation a new modular exponentiation method is presented. Since forward and backward transformation calculations do not need to be performed for every multiplication carried during the exponentiation, the asymptotic crossover for modular exponentiation is decreased to cryptographic sizes. The method yields an efficient and highly parallel architecture for hardware implementations of public-key cryptosystems