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Recent Progress on Coppersmith’s Lattice-Based Method: A Survey

  • Yao LuEmail author
  • Liqiang Peng
  • Noboru Kunihiro
Chapter
Part of the Mathematics for Industry book series (MFI, volume 29)

Abstract

In 1996, Coppersmith proposed a lattice-based method to solve the small roots of a univariate modular equation in polynomial time. Since its invention, Coppersmith’s method has become an important tool in the cryptanalysis of RSA crypto algorithm and its variants. In 2006, Jochemsz and May introduced a general strategy to solve small roots of any form of multivariate modular equations in polynomial time. Based on Jochemsz–May’s strategy, for any given multivariate equations one can easily construct the desired lattices with triangular matrix basis. However, for some attacks, Jochemsz–May’s general strategy could not fully capture the algebraic structure of the target polynomials. Thus, some sophisticated techniques that can deeply exploit the algebraic relations have been proposed. In this paper, we give a survey of these recent approaches for lattice constructions, and also give small examples to show how these approaches work.

Keywords

Coppersmith’s method Unraveled linearization technique Exponent trick Two-step lattice-based method Small roots of modular equations 

References

  1. 1.
    A. Bauer, D. Vergnaud, J. Zapalowicz, Inferring sequences produced by nonlinear pseudorandom number generators using Coppersmith’s methods, in PKC 2012 (2012), pp. 609–626Google Scholar
  2. 2.
    J. Blömer, A. May, New partial key exposure attacks on RSA, in CRYPTO 2003 (2003), pp. 27–43Google Scholar
  3. 3.
    D. Boneh, G. Durfee, Cryptanalysis of RSA with private key \(d\) less than \(N^{0.292}\). IEEE Trans. Inf. Theory 46(4), 1339–1349 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    D. Boneh, G. Durfee, Y. Frankel, An attack on RSA given a small fraction of the private key bits, in ASIACRYPT 1998 (1998), pp. 25–34Google Scholar
  5. 5.
    H. Cohn, N. Heninger, Approximate common divisors via lattices, in ANTS-X (2012)Google Scholar
  6. 6.
    D. Coppersmith, Finding a small root of a bivariate integer equation; factoring with high bits known, in EUROCRYPT 1996 (1996), pp. 178–189Google Scholar
  7. 7.
    D. Coppersmith, Finding a small root of a univariate modular equation, in EUROCRYPT 1996 (1996), pp. 155–165Google Scholar
  8. 8.
    J. Coron, A. May, Deterministic polynomial-time equivalence of computing the RSA secret key and factoring. J. Cryptol. 20(1), 39–50 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    J. Coron, A. Joux, I. Kizhvatov, D. Naccache, P. Paillier, Fault attacks on RSA signatures with partially unknown messages, in CHES 2009 (2009), pp. 444–456Google Scholar
  10. 10.
    J. Coron, D. Naccache, M. Tibouchi, Fault attacks against EMV signatures, in CT-RSA 2010 (2010), pp. 208–220Google Scholar
  11. 11.
    M.J. Coster, B.A. LaMacchia, A.M. Odlyzko, An improved low-density subset sum algorithm, in EUROCRYPT 1991 (1991), pp. 54–67Google Scholar
  12. 12.
    G. Durfee, P.Q. Nguyen, Cryptanalysis of the RSA schemes with short secret exponent from Asiacrypt’99, in ASIACRYPT 2000 (2000), pp. 14–29Google Scholar
  13. 13.
    M. Ernst, E. Jochemsz, A. May, B. de Weger, Partial key exposure attacks on RSA up to full size exponents, in EUROCRYPT 2005 (2005), pp. 371–384Google Scholar
  14. 14.
    P.A. Fouque, N. Guillermin, D. Leresteux, M. Tibouchi, J.C. Zapalowicz, Attacking RSA-CRT signatures with faults on montgomery multiplication. J. Cryptogr. Eng. 3(1), 59–72 (2013)CrossRefzbMATHGoogle Scholar
  15. 15.
    M. Herrmann, Improved cryptanalysis of the multi-prime \(\phi \)-hiding assumption, in AFRICACRYPT 2011 (2011), pp. 92–99Google Scholar
  16. 16.
    M. Herrmann, Lattice-based cryptanalysis using unravelled linearization. Ph.D. thesis, der Ruhr-Universitat Bochum (2011), http://www-brs.ub.ruhr-uni-bochum.de/netahtml/HSS/Diss/HerrmannMathias/diss.pdf
  17. 17.
    M. Herrmann, A. May, Solving linear equations modulo divisors: on factoring given any bits, in ASIACRYPT 2008 (2008), pp. 406–424Google Scholar
  18. 18.
    M. Herrmann, A. May, Attacking power generators using unravelled linearization: when do we output too much? in ASIACRYPT 2009 (2009), pp. 487–504Google Scholar
  19. 19.
    M. Herrmann, A. May, Maximizing small root bounds by linearization and applications to small secret exponent RSA, in PKC 2010 (2010), pp. 53–69Google Scholar
  20. 20.
    N. Howgrave-Graham, Finding small roots of univariate modular equations revisited, in Cryptography and Coding 1997 (1997), pp. 131–142Google Scholar
  21. 21.
    N. Howgrave-Graham, Approximate integer common divisors, in CaLC 2001 (2001), pp. 51–66Google Scholar
  22. 22.
    Z. Huang, L. Hu, J. Xu, Attacking RSA with a composed decryption exponent using unravelled linearization, in Inscrypt 2014 (2014), pp. 207–219Google Scholar
  23. 23.
    E. Jochemsz, A. May, A strategy for finding roots of multivariate polynomials with new applications in attacking RSA variants, in ASIACRYPT 2006 (2006), pp. 267–282Google Scholar
  24. 24.
    E. Jochemsz, A. May, A polynomial time attack on RSA with private CRT-exponents smaller than \(N^{0.073}\), in CRYPTO 2007 (2006), pp. 395–411Google Scholar
  25. 25.
    T. Kleinjung, K. Aoki, J. Franke, A.K. Lenstra, E. Thomé, J.W. Bos, P. Gaudry, A. Kruppa, P.L. Montgomery, D.A. Osvik, H.J.J. te Riele, A. Timofeev, P. Zimmermann, Factorization of a 768-bit RSA modulus, in CRYPTO 2010 (2010), pp. 333–350Google Scholar
  26. 26.
    N. Kunihiro, On optimal bounds of small inverse problems and approximate GCD problmes with higher degree, in ISC 2012 (2012), pp. 55–69Google Scholar
  27. 27.
    N. Kunihiro, K. Kurosawa, Deterministic polynomial time equivalence between factoring and key-recovery attack on Takagi’s RSA, in PKC 2007 (2007), pp. 412–425Google Scholar
  28. 28.
    N. Kunihiro, N. Shinohara, T. Izu, A unified framework for small secret exponent attack on RSA. IEICE Trans. 97-A(6), 1285–1295 (2014)Google Scholar
  29. 29.
    J.C. Lagarias, A.M. Odlyzko, Solving low-density subset sum problems. J. ACM 32(1), 229–246 (1985)Google Scholar
  30. 30.
    A.K. Lenstra, H.W. Lenstra, L. Lovász, Factoring polynomials with rational coefficients. Math. Ann. 261(4), 515–534 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    A.K. Lenstra, E. Tromer, A. Shamir, W. Kortsmit, B. Dodson, J.P. Hughes, P.C. Leyland, Factoring estimates for a 1024-bit RSA modulus, in ASIACRYPT 2003 (2003), pp. 55–74Google Scholar
  32. 32.
    Y. Lu, R. Zhang, D. Lin, Factoring RSA modulus with known bits from both \(p\) and \(q\): a lattice method, in NSS 2013 (2013), pp. 393–404Google Scholar
  33. 33.
    Y. Lu, R. Zhang, D. Lin, Factoring multi-power RSA modulus \(N=p^rq\) with partial known bits, in ACISP 2013 (2013), pp. 57–71Google Scholar
  34. 34.
    Y. Lu, R. Zhang, D. Lin, New partial key exposure attacks on CRT-RSA with large public exponents, in ACNS 2014 (2014), pp. 151–162Google Scholar
  35. 35.
    Y. Lu, R. Zhang, L. Peng, D. Lin, Solving linear equations modulo unknown divisors: revisited, in ASIACRYPT 2015, Part I (2015), pp. 189–213Google Scholar
  36. 36.
    A. May, New RSA vulnerabilities using lattice reduction methods. Ph.D. thesis, University of Paderborn (2003), http://ubdata.uni-paderborn.de/ediss/17/2003/may/disserta.pdf
  37. 37.
    A. May, Secret exponent attacks on RSA-type schemes with moduli \(N=p^rq\), in PKC 2004 (2004), pp. 218–230Google Scholar
  38. 38.
    A. May, Computing the RSA secret key is deterministic polynomial time equivalent to factoring, in CRYPTO 2004 (2004), pp. 213–219Google Scholar
  39. 39.
    A. May, M. Ritzenhofen, Implicit factoring: on polynomial time factoring given only an implicit hint, in Proceedings of the PKC 2009 (2009), pp. 1–14Google Scholar
  40. 40.
    A.J. Menezes, P.C. van Oorschot, S.A. Vanstone, Handbook of Applied Cryptography (CRC Press, Boca Raton, 1996), pp. 118–122CrossRefzbMATHGoogle Scholar
  41. 41.
    P.Q. Nguyen, B. Vallée (eds.), The LLL Algorithm - Survey and Applications. Information Security and Cryptography (Springer, Heidelberg, 2010)Google Scholar
  42. 42.
    L. Peng, L. Hu, J. Xu, Z. Huang, Y. Xie, Further improvement of factoring RSA moduli with implicit hint, in AFRICACRYPT 2014 (2014), pp. 165–177Google Scholar
  43. 43.
    L. Peng, L. Hu, Y. Lu, H. Wei, An improved analysis on three variants of the RSA cryptosystem. To appear in Inscrypt (2016)Google Scholar
  44. 44.
    L. Peng, L. Hu, Y. Lu, J. Xu, Z. Huang, Cryptanalysis of dual RSA. Des. Codes Cryptogr. (2016). doi: 10.1007/s10623-016-0196-5
  45. 45.
    R.L. Rivest, A. Shamir, L.M. Adleman, A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  46. 46.
    S. Sarkar, Small secret exponent attack on RSA variant with modulus \(N=p^rq\). Des. Codes Cryptogr. 73(2), 383–392 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  47. 47.
    S. Sarkar, Revisiting prime power RSA. Discret. Appl. Math. 203, 127–133 (2016)Google Scholar
  48. 48.
    S. Sarkar, S. Maitra, Partial key exposure attack on CRT-RSA, in ACNS 2009 (2009), pp. 473–484Google Scholar
  49. 49.
    S. Sarkar, S. Maitra, Approximate integer common divisor problem relates to implicit factorization. IEEE Trans. Inf. Theory 57(6), 4002–4013 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  50. 50.
    P.W. Shor, Algorithms for quantum computation: discrete log and factoring, in FOCS 1994 (1994), pp. 124–134Google Scholar
  51. 51.
    H. Sun, M. Wu, W. Ting, M.J. Hinek, Dual RSA and its security analysis. IEEE Trans. Inf. Theory 53(8), 2922–2933 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  52. 52.
    A. Takayasu, N. Kunihiro, Better lattice constructions for solving multivariate linear equations modulo unknown divisors, in ACISP 2013 (2013), pp. 118–135Google Scholar
  53. 53.
    A. Takayasu, N. Kunihiro, Cryptanalysis of RSA with multiple small secret exponents, in ACISP 2014 (2014), pp. 176–191Google Scholar
  54. 54.
    A. Takayasu, N. Kunihiro, Partial key exposure attacks on RSA: achieving the Boneh-Durfee bound, in SAC 2014 (2014), pp. 345–362Google Scholar
  55. 55.
    A. Takayasu, N. Kunihiro, Partial key exposure attacks on CRT-RSA: better cryptanalysis to full size encryption exponents, in ACNS 2015 (2015), pp. 518–537Google Scholar
  56. 56.
    A. Takayasu, N. Kunihiro, Partial key exposure attacks on RSA with multiple exponent pairs, in ACISP 2016 (2016), pp. 243–257Google Scholar
  57. 57.
    A. Takayasu, N. Kunihiro, How to generalize RSA cryptanalysis, in PKC 2016, Part II (2016), pp. 67–97Google Scholar
  58. 58.
    A. Takayasu, N. Kunihiro, Partial key exposure attacks on CRT-RSA: general improvement for the exposed least significant bits, in ISC 2016 (2016), pp. 35–47Google Scholar
  59. 59.
    A. Takayasu, N. Kunihiro, Small secret exponent attacks on RSA with unbalanced prime factors, in ISITA 2016 (2016), pp. 236–240Google Scholar
  60. 60.
    A. Takayasu, N. Kunihiro, A tool kit for partial key exposure attacks on RSA. To appear in CT-RSA 2017 (2017)Google Scholar
  61. 61.
    A. Takayasu, N. Kunihiro, General bounds for small inverse problems and its applications to multi-prime RSA. IEICE Trans. 100-A(1), 50–61 (2017)Google Scholar
  62. 62.
    A. Takayasu, Y. Lu, L. Peng, Small CRT-exponent RSA revisited. To appear in EUROCRYPT 2017 (2017)Google Scholar
  63. 63.
    K. Tosu, N. Kunihiro, Optimal bounds for multi-prime \(\phi \)-hiding assumption, in ACISP 2012 (2012), pp. 1–14Google Scholar
  64. 64.
    M. van Dijk, C. Gentry, S. Halevi, V. Vaikuntanathan, Fully homomorphic encryption over the integers, in EUROCRYPT 2010 (2010), pp. 24–43Google Scholar
  65. 65.
    M.J. Wiener, Cryptanalysis of short RSA secret exponents. IEEE Trans. Inf. Theory 36(3), 553–558 (1990)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Frontier SciencesUniversity of TokyoKashiwa-shi, ChibaJapan
  2. 2.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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