Complexity of Lattice Problems: A Cryptographic Perspective by Daniele Micciancio

By Daniele Micciancio

The ebook provides a self-contained review of the state-of-the-art within the complexity of lattice difficulties, with specific emphasis on difficulties which are with regards to the development of cryptographic features. particular issues lined are the most powerful identified inapproximability outcome for the shortest vector challenge; the kinfolk among this and different computational lattice difficulties; an exposition of the way cryptographic features should be outfitted and end up safe in line with worst-case hardness assumptions approximately lattice difficulties; and a learn of the bounds of non-approximability of lattice difficulties. a few heritage in complexity concept, yet no earlier wisdom approximately lattices, is thought.

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5). This completes the proof that the vectors of a reduced basis are as short as possible. 2 Gauss' algorithm In this subsection we describe an algorithm to find a reduced basis for any 2-dimensional lattice. 3, works by computing a sequence of bases satisfying the following property. 2 A basis [a, b) is well ordered if ll a ll ::; ll a - b ll < ll bl l . C([a , b] ) . This is easily accomplished by a simple case analysis. (See part of the 28 COMPLEXITY OF LATTICE PROBLEMS Input: two linearly independent vectors a and b .

Therefore at the end of each iteration B is a basis for the input lattice. 35 Approximation algorithms Input: Lattice basis B = [b 1 , . . C(B ) . ( loop ) : for i = 1 , . . , n for j = i - 1, . . , 1 bi : = bi - Ci ,j bj where Ci,j = l(bi , bj ) / (bj , bj)l f ( i 8 i l l 7r bi ) ll 2 > ll 7ri ( bi + I ) II2 for some i then swap bi and bi +1 and go to ( loop) else output B . Figure 2. 4. e. , the transformation B -+ B ' defined above does not change the orthogonalized vectors hi . However, one can easily check that after the transformation B -+ B ' all Gram­ Schmidt coefficients Jl.

E. , a nonzero lattice vector of length at most 1( n ) · A1. Finally, in Section 3 we use the LLL algorithm to approximately solve CVP. Also for CVP, the ( worst case) approximation factor achieved is 0 ({ 2 / J3) n) where n is the rank of the lattice. Section 4 concludes the chapter with an overview of the latest developments in the design of approximation algorithms for lattice problems, and ( exponential time ) algorithms to solve lattice problems exactly. D. , Complexity of Lattice Problems © Kluwer Academic Publishers 2002 24 COMPLEXITY OF LATTICE PROBLEMS 1.

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