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Solving low-density subset sum problems

Published:01 January 1985Publication History
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Abstract

The subset sum problem is to decide whether or not the 0-l integer programming problem Σni=l aixi = M, ∀I, xI = 0 or 1, has a solution, where the ai and M are given positive integers. This problem is NP-complete, and the difficulty of solving it is the basis of public-key cryptosystems of knapsack type. An algorithm is proposed that searches for a solution when given an instance of the subset sum problem. This algorithm always halts in polynomial time but does not always find a solution when one exists. It converts the problem to one of finding a particular short vector v in a lattice, and then uses a lattice basis reduction algorithm due to A. K. Lenstra, H. W. Lenstra, Jr., and L. Lovasz to attempt to find v. The performance of the proposed algorithm is analyzed. Let the density d of a subset sum problem be defined by d = n/log2(maxi ai). Then for “almost all” problems of density d < 0.645, the vector v we searched for is the shortest nonzero vector in the lattice. For “almost all” problems of density d < 1/n, it is proved that the lattice basis reduction algorithm locates v. Extensive computational tests of the algorithm suggest that it works for densities d < dc(n), where dc(n) is a cutoff value that is substantially larger than 1/n. This method gives a polynomial time attack on knapsack public-key cryptosystems that can be expected to break them if they transmit information at rates below dc(n), as n → ∞.

References

  1. 1 ADLEMAN, L.M. the 15th ACM SjmTposium on Theory of Computing (Boston, Mass., Apr. 25-27). ACM, New York, 1983, 402-412. Google ScholarGoogle Scholar
  2. 2 AFFLERBACH, U Minkowskische Reduktionsbedingungen fiir positiv definite quadratische Formen in 5 Variabeln. Monatsh. Math. 94 (1982), 1-8.Google ScholarGoogle Scholar
  3. 3 BRENT, R.P. A Fortran multiple-precision arithmetic package. ACM Trans. Math. Sofiw. 4, 1 (Mar. 1978), 57-70. Google ScholarGoogle Scholar
  4. 4 BRENTJES, A.J. Multi-dimensional continued fraction algorithms. Mathematical Centre Tract No. 145, Matematisch Centrum, Amsterdam, The Netherlands, 1981.Google ScholarGoogle Scholar
  5. 5 BRICKELL, E. Are most low density knapsacks solvable in polynomial time? In Proceedings of the 14th Southeastern Conference on Combinatorics, Graph Theory, and Computing, 1983. Congressus Numerantium, Vol. 39, 1983, pp. 145-156.Google ScholarGoogle Scholar
  6. 6 DIETER, U.How to calculate shortest vectors in a lattice. Math. Comput. 29 (1975), 827-833.Google ScholarGoogle Scholar
  7. 7 FERGUSON, H. R. P., AND FORCADE, R. W.Multidimensional Euclidean algorithms. J. reine angew. Math. 344 (1982), 171-181.Google ScholarGoogle Scholar
  8. 8 GAREY, M.R., AND JOHNSON, D.S.Computers and Intractability: A Guide to the Theory of NP- Completeness. W. H. Freeman and Co., San Francisco, 1979. Google ScholarGoogle Scholar
  9. 9 KALTOFEN, E.On the complexity of finding short vectors in integer lattices. In Computer Algebra: Proceedings of EUROCAL '83, European Computer Algebra Conference, J. A. VanHulzen, Ed. Lecture Notes in Computer Science, vol. 162. Springer-Verlag, New York, 1983, pp. 236-244. Google ScholarGoogle Scholar
  10. 10 LAGARIAS, J.C.The computational complexity of simultaneous Diophantine approximation problems. SIAM J Comput. 14 (1985), to be published. Google ScholarGoogle Scholar
  11. 11 LAGARIAS, J.C.Knapsack-type public key cryptosystems and Diophantine approximation, (Extended Abstract). In Advances in Cryptology, Proceedings of CRYPTO-83 (Santa Barbara, Aug.), D. Chaum, Ed. Plenum, New York, 1984, pp. 3-24.Google ScholarGoogle Scholar
  12. 12 LEMPEL, A. Cryptography in transition: A survey. Comput. Surv. 11 (1979), 285-304. Google ScholarGoogle Scholar
  13. 13 LENSTRA, A.K., I.ENSTRA, H.W., JR., AND LOVASZ, L.Factoring polynomials with rational coefficients. Math. Annalen 261 (1982), 515-534.Google ScholarGoogle Scholar
  14. 14 MAZO, J.E., AND ODLYZKO, A.M.Lattice points in high-dimensional spheres, paper in preparation.Google ScholarGoogle Scholar
  15. 15 MERKLE, R.C., AND HELLMAN, M.E.Hiding information and signatures in trap-door knapsacks. IEEE Trans. Inf. Theory IT-24 (1978), 525-530.Google ScholarGoogle Scholar
  16. 16 ODLYZKO, A.M.Cryptanalytic attacks on the multiplicative knapsack cryptosystem and on Shamir's fast signature scheme. IEEE Trans. Inf. Theory IT-30, 4 (July I984), 584-601. Google ScholarGoogle Scholar
  17. 17 SHAMIR, A.A polynomial time algorithm for breaking the Merkle-Hellman cryptosystem. In Proceedings of the 23rd IEEE Symposium on Foundations of Computer Science. IEEE, New York, 1982, pp. 145-152.Google ScholarGoogle Scholar
  18. 18 SHAMIR, A.Embedding cryptographic trapdoors in arbitrary knapsack systems. Inf. Proc. Lett. 17 (1983), 77-79.Google ScholarGoogle Scholar
  19. 19 WILF, H.S.Backtrack: An O( 1 ) expected time algorithm for the graph coloring problem. Inf. Proc. Lett. 18 (1984), 11'9-121.Google ScholarGoogle Scholar

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  1. Solving low-density subset sum problems

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        Eugene D. Denman

        This paper discusses the subset sum problem: :9I where a i and M are integers. This problem is NP-complete and is related to the public-key cryptosystem of the knapsack type. The authors propose a short vector lattice method (Algorithm SV) where a set of basis vector b i is constructed for an n+1 dimensional lattice L = L( a, M). A reduced basis is then computed using the L 3 algorithm of Lenstra, Lenstra, and Lova´sz [1]. The authors show that “almost all problems of densities d < 0.645 and d < 1/ n can be solved by the SV algorithm. The paper includes an analysis of the algorithm as well as some computational results. The paper should be of interest to researchers working on data encryption.

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          cover image Journal of the ACM
          Journal of the ACM  Volume 32, Issue 1
          Jan. 1985
          246 pages
          ISSN:0004-5411
          EISSN:1557-735X
          DOI:10.1145/2455
          Issue’s Table of Contents

          Copyright © 1985 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 1 January 1985
          Published in jacm Volume 32, Issue 1

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