Asymptotic granularity reduction and its application
作者: Shenghui SuShuwang LüXiubin Fan
作者单位: 1College of Computer, Beijing University of Technology, Beijing 100124, PR China
2Graduate School, Chinese Academy of Sciences, Beijing 100039, PR China
3Institute of Software, Chinese Academy of Sciences, Beijing 100080, PR China
刊名: Theoretical Computer Science, 2011, Vol.412 (39), pp.5374-5386
来源数据库: Elsevier Journal
DOI: 10.1016/j.tcs.2011.06.008
关键词: Public key cryptosystemTranscendental logarithm problemAsymptotic granularity reductionPolynomial time reductionProvable security
英文摘要: Abstract(#br)It is well known that the inverse function of y = x with the derivative y ′ = 1 is x = y , the inverse function of y = c with the derivative y ′ = 0 is nonexistent, and so on. Hence, on the assumption that the noninvertibility of the univariate increasing function y = f ( x ) with x > 0 is in direct proportion to the growth rate reflected by its derivative, the authors put forward a method of comparing difficulties in inverting two functions on a continuous or discrete interval called asymptotic granularity reduction (AGR) which integrates asymptotic analysis with logarithmic granularities, and is an extension and a complement to polynomial time (Turing) reduction (PTR). Prove by AGR that inverting y ≡ x x ( mod p ) is...
全文获取路径: Elsevier  (合作)
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影响因子:0.489 (2012)

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