Efficient Implementation of Rns Montgomery Multiplication Using Balanced Rns Bases


Authors

Sakineh Sharifi - Department of Information Technology Engineering, Qom University, Qom, Iran. Mohammad Esmaeildoust - Faculty of Marine Engineering, Khorramshahr University of Marine Science and Technology, Iran. Mohammad Reza Taheri - Nanotechnology and Quantum Computing Laboratory, Shahid Beheshti University, GC, Tehran, Iran. Keivan Navi - Faculty of Electrical and Computer Engineering, Shahid Beheshti University GC, Tehran, Iran.


Abstract

Point multiplication is the most important part of elliptic curve cryptography which consumes remarkable time of implementation. Therefore efficiency enhancement of entire system is depending on efficiency of this part. Increasing the efficiency of the modular multiplication improve overall performance of the cryptographic system as it frequency used in some application such as Elliptic Curve Cryptography. By applying Residue Number System (RNS) to Montgomery multiplication as a method for modular multiplication, delay of modular multiplication will be reduced. Appropriate RNS moduli sets replace time consuming operation of multiplication by smaller operations. In this paper two balanced moduli set with proper dynamic range is presented and the efficiency of conversion from RNS to RNS which is the most time consuming part of the Montgomery modular multiplication will be increased.


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ISRP Style

Sakineh Sharifi, Mohammad Esmaeildoust, Mohammad Reza Taheri, Keivan Navi, Efficient Implementation of Rns Montgomery Multiplication Using Balanced Rns Bases, Journal of Mathematics and Computer Science, 12 (2014), no. 1, 51-64

AMA Style

Sharifi Sakineh, Esmaeildoust Mohammad, Taheri Mohammad Reza, Navi Keivan, Efficient Implementation of Rns Montgomery Multiplication Using Balanced Rns Bases. J Math Comput SCI-JM. (2014); 12(1):51-64

Chicago/Turabian Style

Sharifi, Sakineh, Esmaeildoust, Mohammad, Taheri, Mohammad Reza, Navi, Keivan. "Efficient Implementation of Rns Montgomery Multiplication Using Balanced Rns Bases." Journal of Mathematics and Computer Science, 12, no. 1 (2014): 51-64


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