Outlook of Reconfigurable Cryptographic Processing Application Technology

Chapter

Abstract

With the continuous evolution of big data and globalization, the security environment has become increasingly serious. Among them, data security and cryptographic processor security issues are particularly prominent. In terms of data security, the demand for big data processing, represented by cloud computing, has brought new challenges to data security.

References

  1. 1.
    Shor PW (1994) Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings 35th annual symposium on foundations of computer science, pp 124–134Google Scholar
  2. 2.
    Monz T, Nigg D, Martinez EA et al (2015) Realization of a scalable Shor algorithm. Science 351(6277):1068–1070MathSciNetMATHGoogle Scholar
  3. 3.
    Howe J, Moore C, O′Neill M et al (2016) Lattice-based encryption over standard lattices in hardware. In: Proceedings of the 53rd annual design automation conferenceGoogle Scholar
  4. 4.
    Gentry C (2009) Fully homomorphic encryption using ideal lattices. In: Proceedings of the 2009 ACM symposium on theory of computing, pp 169–178Google Scholar
  5. 5.
    Rivest RL, Adleman L, Dertouzos ML (1978) On data banks and privacy homomorphisms. Academic Press, New York, pp 169–180Google Scholar
  6. 6.
    van Dijk M, Gentry C, Halevi S et al (2010) Fully homomorphic encryption over the integers. In: Advances in cryptology-EUROCRYPT 2010: 29th annual international conference on the theory and applications of cryptographic techniquesGoogle Scholar
  7. 7.
    Coron J, Mandal A, Naccache D et al (2011) Fully homomorphic encryption over the integers with shorter public keys. In: Conference on advances in cryptology, pp 487–504CrossRefGoogle Scholar
  8. 8.
    Gentry C, Halevi S (2011) Implementing Gentry’s fully-homomorphic encryption scheme. In: Annual international conference on the theory and applications of cryptographic techniques, pp 129–148CrossRefGoogle Scholar
  9. 9.
    Brakerski Z, Gentry C, Vaikuntanathan V (2014) Fully homomorphic encryption without bootstrapping. ACM Trans Comput Theory 6(3):13–36MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gentry C, Halevi S, Smart NP (2012) Homomorphic evaluation of the AES circuit. In: The 32nd annual international cryptology conference, pp 850–867Google Scholar
  11. 11.
    Gentry C, Sahai A, Waters B (2013) Homomorphic encryption from learning with errors: conceptually-simpler, asymptotically-faster, attribute-based. In: The 33rd annual international cryptology conference, pp 75–92CrossRefGoogle Scholar
  12. 12.
    Fan J, Vercauteren F (2012) Somewhat practical fully homomorphic encryption. IACR Cryptology ePrint Archive 144Google Scholar
  13. 13.
    Bos JW, Lauter K, Loftus J et al (2013) Improved security for a ring based fully homomorphic encryption scheme. In: The 14th IMA international conference on cryptography and coding, pp 45–64CrossRefGoogle Scholar
  14. 14.
    López-Alt A, Tromer E, Vaikuntanathan V (2012) On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption. In: Proceedings of the forty-fourth annual ACM symposium on theory of computing. ACM, pp 1219–1234Google Scholar
  15. 15.
    Stehl D, Steinfeld R (2011) Making NTRU as secure as worst-case problems over ideal lattices. In: EUROCRYPT 2011 international conference on the theory and applications of cryptographic techniques, pp 27–47Google Scholar
  16. 16.
    Erdinç Öztürk et al (2017) A custom accelerator for homomorphic encryption applications. IEEE Trans Comput 66(1):316MathSciNetMATHGoogle Scholar
  17. 17.
    Aguilar-Melchor C, Barrier J, Guelton S et al (2016) NFLlib: NTT-based fast lattice library. In: Cryptographers track at the RSA conference, pp 341–356CrossRefGoogle Scholar
  18. 18.
    Lepoint T, Naehrig M (2014) A comparison of the homomorphic encryption schemes FV and YASHE. In: The 7th international conference on cryptology in Africa, pp 318–335CrossRefGoogle Scholar
  19. 19.
    Khedr A, Gulak G, Vaikuntanathan V (2016) SHIELD: scalable homomorphic implementation of encrypted data-classifiers. IEEE Trans Comput 65(9):2848–2858MathSciNetCrossRefGoogle Scholar
  20. 20.
    Dai W, Doroz Y, Sunar B (2014) Accelerating NTRU based homomorphic encryption using GPUs. In: 2014 IEEE high performance extreme computing conferenceGoogle Scholar
  21. 21.
    Roy SS et al (2017) Hardware assisted fully homomorphic function evaluation and encrypted search. IEEE Trans Comput PP(99):1Google Scholar
  22. 22.
    Doroz Y, Ozturk E, Sunar B (2015) Accelerating fully homomorphic encryption in hardware. IEEE Trans Comput 64(6):1509–1521MathSciNetMATHGoogle Scholar
  23. 23.
    Wang W, Huang XM, Emmart N et al (2014) VLSI design of a large-number multiplier for fully homomorphic encryption. IEEE Trans Very Large Scale Integr Syst 22(9):1879–1887CrossRefGoogle Scholar
  24. 24.
    Doroz Y, Hu Y, Sunar B (2016) Homomorphic AES evaluation using the modified LTV scheme. Des Codes Crypt 80(2):333–358MathSciNetCrossRefGoogle Scholar
  25. 25.
    Shoup V.NTL: a library for doing number theory[EB/OL]. http://www.shoup.net/ntl/[201731]
  26. 26.
    Hart W.FLINT: fast library for number Theory[EB/OL]. http://www.flintlib.org[201731]
  27. 27.
    Halevi S, Shoup V (2014) Algorithms in HElib. In: The 34th annual international cryptology conference, pp 554–571CrossRefGoogle Scholar
  28. 28.
    Halevi S, Shoup V (2015) Bootstrapping for helib. In: Annual International conference on the theory and applications of cryptographic techniques, pp 641–670Google Scholar
  29. 29.
    Chen H, Laine K, Player R (2016) Simple encrypted arithmetic library-SEAL (v2.1) [EB/OL]. https://www.microsoft.com/enus/research/wpcontent/uploads/2016/09/sealmanual2.pdf[20160901]
  30. 30.
    Wang W, Hu Y, Chen LM et al (2015) Exploring the feasibility of fully homomorphic encryption. IEEE Trans Comput 64(3):698–706MathSciNetCrossRefGoogle Scholar
  31. 31.
    Wei D, Sunar B (2016) cuHE: a homomorphic encryption accelerator library. In: The second international conference on cryptography and information security in the Balkans, pp 169–186Google Scholar
  32. 32.
    Wang W, Huang X M (2013) FPGA implementation of a large-number multiplier for fully homomorphic encryption. In: IEEE international symposium on circuits and systems, pp 2589–2592Google Scholar
  33. 33.
    Poppelmann T, Naehrig M, Putnam A et al (2015) Accelerating homomorphic evaluation on reconfigurable hardware. In: The 17th international workshop on cryptographic hardware and embedded systems, pp 143–163Google Scholar
  34. 34.
    Sinha Roy S, Järvinen K, Vercauteren F et al (2015) Modular hardware architecture for somewhat homomorphic function evaluation. In: The 17th international workshop on cryptographic hardware and embedded systems, pp 164–184Google Scholar
  35. 35.
    Doroz Y, Ozturb E, Sunar B (2014) A million-bit multiplier architecture for fully homomorphic encryption. Microprocess Microsyst 38(8):766–775CrossRefGoogle Scholar
  36. 36.
    Wang W, Hu Y, Chen L et al (2012) Accelerating fully homomorphic encryption using GPU. In: IEEE conference on high performance extreme computing. IEEE, pp 1–5Google Scholar
  37. 37.
    Moore C, O′Neill M, O′Sullivan E, et al (2014) Practical homomorphic encryption: a survey. In: IEEE international symposium on circuits and systems, pp 2792–2795Google Scholar
  38. 38.
    Cooley JW, Tukey JW (1965) An algorithm for the machine calculation of complex Fourier series. Math Comput 19(90):297–301MathSciNetCrossRefGoogle Scholar
  39. 39.
    Bhunia S, Hsiao MS, Banga M et al (2014) Hardware trojan attacks: threat analysis and countermeasures. Proc IEEE 102(8):1229–1247CrossRefGoogle Scholar
  40. 40.
    Chakraborty RS, Narasimhan S, Bhunia S (2009) Hardware Trojan: threats and emerging solutions. In: IEEE international high-level design validation and test workshop. IEEE, pp 166–171Google Scholar
  41. 41.
    Chen Z, Guo X, Nagesh R et al (2008) Hardware Trojan designs on BASYS FPGA board. Embed Syst Chall Contest Cyber Secur Aware WeekGoogle Scholar
  42. 42.
    Lin L, Kasper M, Güneysu T et al (2009) Trojan side-channels: lightweight hardware trojans through side-channel engineering. Springer, New York, pp 382–395Google Scholar
  43. 43.
    Lin L, Burleson W, Paar C (2009) MOLES: malicious off-chip leakage enabled by side-channels. In: Proceedings of the 2009 international conference on computer-aided design, pp 117–122Google Scholar
  44. 44.
    Bhasin S, Danger J, Guilley S et al (2013) Hardware Trojan horses in cryptographic IPcores. In: Workshop on fault diagnosis and tolerance in cryptography, pp 15–29Google Scholar
  45. 45.
    Piret G, Quisquater J (2003) A differential fault attack technique against SPN structures, with application to the AES and KHAZAD. In: International workshop on cryptographic hardware and embedded systems, pp 77–88Google Scholar
  46. 46.
    Banga M, Hsiao MS (2010) Trusted RTL: Trojan detection methodology in pre-silicon designs. In: 2010 IEEE international symposium on hardware-oriented security and trust (HOST). IEEE, pp 56–59Google Scholar
  47. 47.
    Love E, Jin Y, Makris Y (2012) Proofcarrying hardware intellectual property: a pathway to trusted module acquisition. IEEE Trans Inf Forensics Secur 7(1):25–40CrossRefGoogle Scholar
  48. 48.
    Singh B, Shankar A, Wolff F et al (2014) Cross-correlation of specification and RTL for soft IP analysis. In: Proceedings of the conference on design, automation and test in Europe, European design and automation association, p 290Google Scholar
  49. 49.
    Chakraborty RS, Wolff F, Paul S et al (2009) MERO: A statistical approach for hardware Trojan detection. Springer, New York, pp 396–410Google Scholar
  50. 50.
    Narasimhan S, Du D, Chakraborty RS et al (2013) Hardware Trojan detection by multiple-parameter side-channel analysis. IEEE Trans Comput 62(11):2183–2195MathSciNetCrossRefGoogle Scholar
  51. 51.
    Rai D, Lach J (2009) Performance of delay-based Trojan detection techniques under parameter variations. In: IEEE international workshop on hardware-oriented security and trust, pp 58–65Google Scholar
  52. 52.
    Chakraborty RS, Bhunia S (2011) Security against hardware trojan attacks using key-based design obfuscation. J Electron Test 27(6):767–785CrossRefGoogle Scholar
  53. 53.
    Xiao K, Tehranipoor M (2013) BISA: Built-in self-verification for preventing hardware trojan insertion. In: IEEE international workshop on hardware-oriented security and trust, pp 45–50Google Scholar
  54. 54.
    Salmani H, Tehranipoor M, Plusquellic J (2012) A novel technique for improving hardware Trojan detection and reducing trojan activation time. IEEE Trans Very Large Scale Integr VLSI Syst 20(1):112–125CrossRefGoogle Scholar
  55. 55.
    Waksman A, Sethumadhavan S (2010) Tamper evident microprocessors. In: IEEE symposium on security and privacy (SP), pp 173–188Google Scholar
  56. 56.
    MalSarkar S, Krishna A, Ghosh A et al (2014) Hardware trojan attacks in FPGA devices: threat analysis and effective counter measures. In: Proceedings of the 24th edition of the Great Lakes symposium on VLSI, pp 287–292Google Scholar
  57. 57.
    Zhang J, Su G, Liu Y et al (2014) On Trojan side channel design and identification. In: 2014 IEEE/ACM international conference on computer-aided design, pp 278–285Google Scholar
  58. 58.
    Voitsechov D, Etsion Y (2014) Single-graph multiple flows: energy efficient design alternative for GPGPUs. In: ACM SIGARCH computer architecture news, pp 205–216CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. and Science Press, Beijing 2018

Authors and Affiliations

  1. 1.Institute of MicroelectronicsTsinghua UniversityBeijingChina

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