On the Optimal Computaion of Finite Field Exponentiation

It has been shown that the optimal computation of finite field exponentiation is closely related to the problem of finding a suitable addition chain with the shortest possible length. However, obtaining the shortest addition chain for a given arbitrary exponent is a NP-hard problem. Hence in general, we are forced to use some kind of heuristic in order to compute field exponentiation with a semi-optimal number of underlying arithmetic operations. In this paper we present a novel heuristic for that problem which is based on an immune artificial system strategy. The results obtained by our scheme yield the shortest reported lengths for the exponents typically used when computing field multiplicative inverses for error-correcting and elliptic curve cryptographic applications.

[1]  Kazuyoshi Takagi,et al.  A Fast Algorithm for Multiplicative Inversion in GF(2m) Using Normal Basis , 2001, IEEE Trans. Computers.

[2]  L. Segel,et al.  Design Principles for the Immune System and Other Distributed Autonomous Systems , 2001 .

[3]  S. Forrest,et al.  Immunology as Information Processing , 2001 .

[4]  T. Itoh,et al.  A Fast Algorithm for Computing Multiplicative Inverses in GF(2^m) Using Normal Bases , 1988, Inf. Comput..

[5]  Donald Ervin Knuth,et al.  The Art of Computer Programming , 1968 .

[6]  Alfred Menezes,et al.  Handbook of Applied Cryptography , 2018 .

[7]  C. A. Coello Coello,et al.  A parallel implementation of an artificial immune system to handle constraints in genetic algorithms: preliminary results , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[9]  Daniel M. Gordon,et al.  A Survey of Fast Exponentiation Methods , 1998, J. Algorithms.

[10]  Gary B. Lamont,et al.  A distributed architecture for a self-adaptive computer virus immune system , 1999 .

[11]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[12]  R. Gershon,et al.  "Clonal selection and after," and after. , 1979, The New England journal of medicine.

[13]  Naruaki Toma,et al.  Immune algorithm with immune network and MHC for adaptive problem solving , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[14]  J. Wrench Table errata: The art of computer programming, Vol. 2: Seminumerical algorithms (Addison-Wesley, Reading, Mass., 1969) by Donald E. Knuth , 1970 .