By Daniele Micciancio
Lattices are geometric gadgets that may be pictorially defined because the set of intersection issues of an enormous, normal n-dimensional grid. De spite their obvious simplicity, lattices disguise a wealthy combinatorial struc ture, which has attracted the eye of serious mathematicians during the last centuries. no longer strangely, lattices have came across various ap plications in arithmetic and desktop technology, starting from quantity conception and Diophantine approximation, to combinatorial optimization and cryptography. The research of lattices, in particular from a computational perspective, used to be marked by way of significant breakthroughs: the advance of the LLL lattice relief set of rules via Lenstra, Lenstra and Lovasz within the early 80's, and Ajtai's discovery of a connection among the worst-case and average-case hardness of yes lattice difficulties within the past due 90's. The LLL set of rules, regardless of the quite terrible caliber of the answer it provides within the worst case, allowed to plan polynomial time ideas to many classical difficulties in machine technological know-how. those comprise, fixing integer courses in a hard and fast variety of variables, factoring polynomials over the rationals, breaking knapsack established cryptosystems, and discovering recommendations to many different Diophantine and cryptanalysis difficulties.
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Extra info for Complexity of Lattice Problems: A Cryptographic Perspective (The Springer International Series in Engineering and Computer Science)
In the rest of this section II · II is an arbitrary, but fixed, norm. The input to the algorithm is a pair of linearly independent (integer) vectors a , b . We want to find a new basis [a', b 'J for £([a, b]) such that ll a' ll = ) q and l i b' II = A2 , where At and A2 are the minima of the lattice with respect to II · II . 1 we introduce a notion of reduced basis (for two dimensional lattices), and prove that a basis is reduced if and only if the basis vectors have length At and A2 · In Subsection 1 .
Xn is a solution to the subset sum problem. Notice that this algorithm can be succinctly described as follows: = 1 Multiply the subset sum problem by some large constant c to obtain an equivalent subset sum instance ( c · a1, . . , c · an, c s ) · 2 Reduce ( c · a1, . . , c · an, c · s ) to a CVP instance (B, t) using the reduction described in the proof of Theorem 3. 1 . ) using the following heuris tics 1 : in order to find the lattice vector closest to t, look for a short vector in the lattice generated by L [Bit).
Reductions between promise problems are defined in the obvious way. , f (IIvEs) � li YES and f (liNo) � li�w · Clearly any al gorithm A to solve (liYES • IIN0 ) can be used to solve (II YES , liNo) as follows: on input I E livES UliNo, run A on f (I) and output the result. Notice that f (I) always satisfy the promise J (I) E liYES U li N O • and f (I) is a YES instance if and only if I is a YES instance. A promise problem A is NP-hard if any NP language (or, more generally, any NP promise problem) B can be efficiently reduced to A.