Skip to main content
SpringerLink
Log in

A New Approach to Efficient and Secure Fixed-Point Computation

  • Conference paper
  • First Online:
Applied Cryptography and Network Security (ACNS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14583))

Included in the following conference series:

Abstract

Secure Multi-Party Computation (MPC) constructions typically allow computation over a finite field or ring. While useful for many applications, certain real-world applications require the usage of decimal numbers. While it is possible to emulate floating-point operations in MPC, fixed-point computation has gained more traction in the practical space due to its simplicity and efficient realizations. Even so, current protocols for fixed-point MPC still require computing a secure truncation after each multiplication gate. In this paper, we show a new paradigm for realizing fixed-point MPC. Starting from an existing MPC protocol over arbitrary, large, finite fields or rings, we show how to realize MPC over a residue number system (RNS). This allows us to leverage certain mathematical structures to construct a secure algorithm for efficient approximate truncation by a static and public value. We then show how this can be used to realize highly efficient secure fixed-point computation. In contrast to previous approaches, our protocol does not require any multiplications of secret values in the underlying MPC scheme to realize truncation but instead relies on preprocessed pairs of correlated random values, which we show can be constructed very efficiently, when accepting a small amount of leakage and robustness in the strong, covert model. We proceed to implement our protocol, with SPDZ [28] as the underlying MPC protocol, and achieve significantly faster fixed-point multiplication.

This work has received funding from the Alexandra Institute’s performance contracts for 2021-24 with the Danish Ministry of Higher Education and Science and by Innovation Fund Denmark in Grand Solution CRUCIAL 1063-00001B. Tore and Jonas performed part of their work while at the Alexandra Institute.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Recall that the Irwin-Hall distribution is the distribution of a sum of n independent random variables each of which are uniformly distributed on [0, 1) and that the \({\textbf {Irwin-Hall}}(n) \rightarrow {\textbf {N}}(n/2, n/12)\) as \(n \rightarrow \infty \).

  2. 2.

    Our FRESCO fork is freely available at https://github.com/jonas-lj/fresco and our benchmark setup can be found at https://github.com/jonas-lj/FFTDemo.

  3. 3.

    The factors are for \(\gamma =0.5\) and depend on the size of the domain and whether execution is over WAN/LAN. Concretely the factors are computed by taking the number of triples required for truncation from Table 4 and multiplying with the preprocessing time from Table 3 and adding the online time (again from Table 4).

  4. 4.

    Observe that edaBits require many different components to achieve their efficient result. This includes faulty multiplications in MPC which are about O(B) times more efficient than a “normal” multiplication in MPC. Here \(B\in \{3, 4, 5\}\) depending on an amortization parameter. In the table, we have for simplicity only counted real multiplications and assumed O(B) faulty multiplications are equivalent to a real one.

References

  1. Ieee standard for floating-point arithmetic. IEEE Std 754–2019 (Revision of IEEE 754–2008), pp. 1–84 (2019). https://doi.org/10.1109/IEEESTD.2019.8766229

  2. Abspoel, M., Dalskov, A.P.K., Escudero, D., Nof, A.: An efficient passive-to-active compiler for honest-majority MPC over rings. In: Sako, K., Tippenhauer, N.O. (eds.) ACNS 21, Part II. LNCS, vol. 12727, pp. 122–152. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-030-78375-4_6

  3. Alexandra Institute: FRESCO - a FRamework for Efficient Secure COmputation. https://github.com/aicis/fresco

  4. Algesheimer, J., Camenisch, J., Shoup, V.: Efficient computation modulo a shared secret with application to the generation of shared safe-prime products. In: Yung, M. (ed.) CRYPTO 2002. LNCS, vol. 2442, pp. 417–432. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45708-9_27

  5. Aliasgari, M., Blanton, M., Zhang, Y., Steele, A.: Secure computation on floating point numbers. In: NDSS 2013. The Internet Society, February 2013

    Google Scholar 

  6. Almeida, J.B., et al.: A fast and verified software stack for secure function evaluation. In: Thuraisingham, B.M., Evans, D., Malkin, T., Xu, D. (eds.) ACM CCS 2017, pp. 1989–2006. ACM Press, October/November 2017. https://doi.org/10.1145/3133956.3134017

  7. Asif, S., Hossain, M.S., Kong, Y.: High-throughput multi-key elliptic curve cryptosystem based on residue number system. IET Comput. Digit. Tech. 11(5), 165–172 (2017). https://doi.org/10.1049/iet-cdt.2016.0141

  8. Atallah, M.J., Bykova, M., Li, J., Frikken, K.B., Topkara, M.: Private collaborative forecasting and benchmarking. In: Atluri, V., Syverson, P.F., di Vimercati, S.D.C. (eds.) Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society, WPES 2004, Washington, DC, USA, October 28, 2004, pp. 103–114. ACM (2004). https://doi.org/10.1145/1029179.1029204

  9. Aumann, Y., Lindell, Y.: Security against covert adversaries: efficient protocols for realistic adversaries. J. Cryptol. 23(2), 281–343 (2010). https://doi.org/10.1007/s00145-009-9040-7

    Article  MathSciNet  Google Scholar 

  10. Banerjee, A., Clear, M., Tewari, H.: zkHawk: practical private smart contracts from MPC-based hawk. Cryptology ePrint Archive, Report 2021/501 (2021). https://eprint.iacr.org/2021/501

  11. Baum, C., Chiang, J.H., David, B., Frederiksen, T.K.: Eagle: Efficient privacy preserving smart contracts. IACR Cryptol. ePrint Arch., p. 1435 (2022). https://eprint.iacr.org/2022/1435

  12. Baum, C., David, B., Frederiksen, T.K.: P2DEX: privacy-preserving decentralized cryptocurrency exchange. In: Sako, K., Tippenhauer, N.O. (eds.) ACNS 2021. LNCS, vol. 12726, pp. 163–194. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78372-3_7

    Chapter  Google Scholar 

  13. Bogetoft, P., et al.: Secure multiparty computation goes live. In: Dingledine, R., Golle, P. (eds.) FC 2009. LNCS, vol. 5628, pp. 325–343. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03549-4_20

    Chapter  Google Scholar 

  14. Boyle, E., et al.: Function secret sharing for mixed-mode and fixed-point secure computation. In: Canteaut, A., Standaert, F.-X. (eds.) EUROCRYPT 2021. LNCS, vol. 12697, pp. 871–900. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77886-6_30

    Chapter  Google Scholar 

  15. Canetti, R.: Universally composable security: a new paradigm for cryptographic protocols. In: 42nd Annual Symposium on Foundations of Computer Science, FOCS 2001, 14–17 October 2001, Las Vegas, Nevada, USA, pp. 136–145. IEEE Computer Society (2001). https://doi.org/10.1109/SFCS.2001.959888

  16. Catrina, O., de Hoogh, S.: Improved primitives for secure multiparty integer computation. In: Garay, J.A., De Prisco, R. (eds.) SCN 2010. LNCS, vol. 6280, pp. 182–199. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15317-4_13

    Chapter  Google Scholar 

  17. Catrina, O., Saxena, A.: Secure computation with fixed-point numbers. In: Sion, R. (ed.) FC 2010. LNCS, vol. 6052, pp. 35–50. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14577-3_6

    Chapter  Google Scholar 

  18. Chandran, N., Gupta, D., Obbattu, S.L.B., Shah, A.: SIMC: ML inference secure against malicious clients at semi-honest cost. Cryptology ePrint Archive, Report 2021/1538 (2021). https://eprint.iacr.org/2021/1538

  19. Chen, M., et al.: Multiparty generation of an RSA modulus. In: Micciancio, D., Ristenpart, T. (eds.) CRYPTO 2020. LNCS, vol. 12172, pp. 64–93. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-56877-1_3

    Chapter  Google Scholar 

  20. Chen, M., et al.: Multiparty generation of an RSA modulus. J. Cryptol. 35(2), 12 (2022). https://doi.org/10.1007/s00145-021-09395-y

  21. Chen, M., et al.: Diogenes: lightweight scalable RSA modulus generation with a dishonest majority. In: 2021 IEEE Symposium on Security and Privacy, pp. 590–607. IEEE Computer Society Press, May 2021. https://doi.org/10.1109/SP40001.2021.00025

  22. Cramer, R., Damgård, I., Escudero, D., Scholl, P., Xing, C.: SPD\(\mathbb{Z}_{2^k}\): efficient MPC mod \(2^k\) for dishonest majority. In: Shacham, H., Boldyreva, A. (eds.) CRYPTO 2018. LNCS, vol. 10992, pp. 769–798. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96881-0_26

    Chapter  Google Scholar 

  23. Dalskov, A.P.K., Escudero, D., Nof, A.: Fast fully secure multi-party computation over any ring with two-thirds honest majority. In: Yin, H., Stavrou, A., Cremers, C., Shi, E. (eds.) ACM CCS 2022, pp. 653–666. ACM Press, November 2022. https://doi.org/10.1145/3548606.3559389

  24. Dalskov, A., Orlandi, C., Keller, M., Shrishak, K., Shulman, H.: Securing DNSSEC keys via threshold ECDSA from generic MPC. In: Chen, L., Li, N., Liang, K., Schneider, S. (eds.) ESORICS 2020. LNCS, vol. 12309, pp. 654–673. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59013-0_32

    Chapter  Google Scholar 

  25. Damgård, I., Damgård, K., Nielsen, K., Nordholt, P.S., Toft, T.: Confidential benchmarking based on multiparty computation. In: Grossklags, J., Preneel, B. (eds.) FC 2016. LNCS, vol. 9603, pp. 169–187. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54970-4_10

    Chapter  Google Scholar 

  26. Damgård, I., Escudero, D., Frederiksen, T.K., Keller, M., Scholl, P., Volgushev, N.: New primitives for actively-secure MPC over rings with applications to private machine learning. In: 2019 IEEE Symposium on Security and Privacy, pp. 1102–1120. IEEE Computer Society Press, May 2019. https://doi.org/10.1109/SP.2019.00078

  27. Damgård, I., Keller, M., Larraia, E., Pastro, V., Scholl, P., Smart, N.P.: Practical covertly secure MPC for dishonest majority – or: breaking the SPDZ limits. In: Crampton, J., Jajodia, S., Mayes, K. (eds.) ESORICS 2013. LNCS, vol. 8134, pp. 1–18. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40203-6_1

    Chapter  Google Scholar 

  28. Damgård, I., Pastro, V., Smart, N., Zakarias, S.: Multiparty computation from somewhat homomorphic encryption. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 643–662. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32009-5_38

    Chapter  Google Scholar 

  29. Delpech de Saint Guilhem, C., Makri, E., Rotaru, D., Tanguy, T.: The return of eratosthenes: secure generation of RSA moduli using distributed sieving. In: Vigna, G., Shi, E. (eds.) ACM CCS 2021, pp. 594–609. ACM Press, November 2021. https://doi.org/10.1145/3460120.3484754

  30. Demmler, D., Schneider, T., Zohner, M.: ABY - a framework for efficient mixed-protocol secure two-party computation. In: NDSS 2015. The Internet Society, February 2015

    Google Scholar 

  31. Deryabin, M., Chervyakov, N., Tchernykh, A., Babenko, M., Shabalina, M.: High performance parallel computing in residue number system. Int. J. Comb. Optim. Problems Inform. 9(1), 62–67 (2018). https://ijcopi.org/ojs/article/view/80

  32. Döttling, N., Ghosh, S., Nielsen, J.B., Nilges, T., Trifiletti, R.: TinyOLE: Efficient actively secure two-party computation from oblivious linear function evaluation. In: Thuraisingham, B.M., Evans, D., Malkin, T., Xu, D. (eds.) ACM CCS 2017, pp. 2263–2276. ACM Press, October/November 2017. https://doi.org/10.1145/3133956.3134024

  33. Du, W., Atallah, M.J.: Privacy-preserving cooperative statistical analysis. In: 17th Annual Computer Security Applications Conference (ACSAC 2001), 11–14 December 2001, New Orleans, Louisiana, USA, pp. 102–110. IEEE Computer Society (2001). https://doi.org/10.1109/ACSAC.2001.991526

  34. Escudero, D., Ghosh, S., Keller, M., Rachuri, R., Scholl, P.: Improved primitives for MPC over mixed arithmetic-binary circuits. In: Micciancio, D., Ristenpart, T. (eds.) CRYPTO 2020. LNCS, vol. 12171, pp. 823–852. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-56880-1_29

    Chapter  Google Scholar 

  35. Fouque, P.-A., Stern, J., Wackers, G.-J.: CryptoComputing with rationals. In: Blaze, M. (ed.) FC 2002. LNCS, vol. 2357, pp. 136–146. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36504-4_10

    Chapter  Google Scholar 

  36. Fournaris, A.P., Papachristodoulou, L., Batina, L., Sklavos, N.: Residue number system as a side channel and fault injection attack countermeasure in elliptic curve cryptography. In: 2016 International Conference on Design and Technology of Integrated Systems in Nanoscale Era, DTIS 2016, Istanbul, Turkey, April 12–14, 2016, pp. 1–4. IEEE (2016). https://doi.org/10.1109/DTIS.2016.7483807

  37. Franz, M., Deiseroth, B., Hamacher, K., Jha, S., Katzenbeisser, S., Schröder, H.: Secure computations on non-integer values. In: 2010 IEEE International Workshop on Information Forensics and Security, WIFS 2010, Seattle, WA, USA, December 12–15, 2010, pp. 1–6. IEEE (2010). https://doi.org/10.1109/WIFS.2010.5711458

  38. Franz, M., Katzenbeisser, S.: Processing encrypted floating point signals. In: Heitzenrater, C., Craver, S., Dittmann, J. (eds.) Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security, MM &Sec ’11, Buffalo, New York, USA, September 29–30, 2011, pp. 103–108. ACM (2011). https://doi.org/10.1145/2037252.2037271

  39. Frederiksen, T.K., Keller, M., Orsini, E., Scholl, P.: A unified approach to MPC with preprocessing using OT. In: Iwata, T., Cheon, J.H. (eds.) ASIACRYPT 2015. LNCS, vol. 9452, pp. 711–735. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48797-6_29

    Chapter  Google Scholar 

  40. Frederiksen, T.K., Lindell, Y., Osheter, V., Pinkas, B.: Fast distributed RSA key generation for semi-honest and malicious adversaries. In: Shacham, H., Boldyreva, A. (eds.) CRYPTO 2018. LNCS, vol. 10992, pp. 331–361. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96881-0_12

    Chapter  Google Scholar 

  41. Frederiksen, T.K., Lindstrøm, J., Madsen, M.W., Spangsberg, A.D.: A new approach to efficient and secure fixed-point computation. IACR Cryptol. ePrint Arch., p. 035 (2024). https://eprint.iacr.org/2024/035

  42. Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game or A completeness theorem for protocols with honest majority. In: Aho, A. (ed.) 19th ACM STOC, pp. 218–229. ACM Press, May 1987. https://doi.org/10.1145/28395.28420

  43. Hazay, C., Ishai, Y., Marcedone, A., Venkitasubramaniam, M.: LevioSA: Lightweight secure arithmetic computation. In: Cavallaro, L., Kinder, J., Wang, X., Katz, J. (eds.) ACM CCS 2019. pp. 327–344. ACM Press, November 2019. https://doi.org/10.1145/3319535.3354258

  44. Hazay, C., Scholl, P., Soria-Vazquez, E.: Low cost constant round MPC combining BMR and oblivious transfer. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 598–628. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70694-8_21

    Chapter  Google Scholar 

  45. Jacquemin, D., Mert, A.C., Roy, S.S.: Exploring RNS for isogeny-based cryptography. IACR Cryptol. ePrint Arch., p. 1289 (2022). https://eprint.iacr.org/2022/1289

  46. Keller, M., Orsini, E., Scholl, P.: MASCOT: Faster malicious arithmetic secure computation with oblivious transfer. In: Weippl, E.R., Katzenbeisser, S., Kruegel, C., Myers, A.C., Halevi, S. (eds.) ACM CCS 2016, pp. 830–842. ACM Press, October 2016. https://doi.org/10.1145/2976749.2978357

  47. Keller, M., Pastro, V., Rotaru, D.: Overdrive: making SPDZ great again. In: Nielsen, J.B., Rijmen, V. (eds.) EUROCRYPT 2018. LNCS, vol. 10822, pp. 158–189. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78372-7_6

    Chapter  Google Scholar 

  48. Kerik, L., Laud, P., Randmets, J.: Optimizing MPC for robust and scalable integer and floating-point arithmetic. In: Clark, J., Meiklejohn, S., Ryan, P.Y.A., Wallach, D., Brenner, M., Rohloff, K. (eds.) FC 2016. LNCS, vol. 9604, pp. 271–287. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53357-4_18

    Chapter  Google Scholar 

  49. Kiltz, E., Leander, G., Malone-Lee, J.: Secure computation of the mean and related statistics. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 283–302. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30576-7_16

    Chapter  Google Scholar 

  50. Larraia, E., Orsini, E., Smart, N.P.: Dishonest majority multi-party computation for binary circuits. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014. LNCS, vol. 8617, pp. 495–512. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44381-1_28

    Chapter  Google Scholar 

  51. Li, S., Xue, K., Zhu, B., Ding, C., Gao, X., Wei, D.S.L., Wan, T.: FALCON: A fourier transform based approach for fast and secure convolutional neural network predictions. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13–19, 2020. pp. 8702–8711. Computer Vision Foundation / IEEE (2020). https://doi.org/10.1109/CVPR42600.2020.00873. https://openaccess.thecvf.com/content_CVPR_2020/html/Li_FALCON_A_Fourier_Transform_Based_Approach_for_Fast_and_Secure_CVPR_2020_paper.html

  52. Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44598-6_3

    Chapter  Google Scholar 

  53. Makri, E., Rotaru, D., Vercauteren, F., Wagh, S.: \(\sf Rabbit\): efficient comparison for secure multi-party computation. In: Borisov, N., Diaz, C. (eds.) FC 2021. LNCS, Part I, vol. 12674, pp. 249–270. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-64322-8_12

    Chapter  Google Scholar 

  54. Mohassel, P., Rindal, P.: ABY\(^3\): a mixed protocol framework for machine learning. In: Lie, D., Mannan, M., Backes, M., Wang, X. (eds.) ACM CCS 2018, pp. 35–52. ACM Press, October 2018. https://doi.org/10.1145/3243734.3243760

  55. Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy, pp. 19–38. IEEE Computer Society Press, May 2017. https://doi.org/10.1109/SP.2017.12

  56. Nielsen, J.B., Orlandi, C.: LEGO for two-party secure computation. In: Reingold, O. (ed.) TCC 2009. LNCS, vol. 5444, pp. 368–386. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00457-5_22

    Chapter  Google Scholar 

  57. Quisquater, J.J.: Fast decipherment algorithm for rsa public-key cryptosystem. Electron. Lett. 18, 905–907(2) (1982). https://digital-library.theiet.org/content/journals/10.1049/el_19820617

  58. Rotaru, D., Smart, N.P., Tanguy, T., Vercauteren, F., Wood, T.: Actively secure setup for SPDZ. J. Cryptol. 35(1), 5 (2022). https://doi.org/10.1007/s00145-021-09416-w

    Article  MathSciNet  Google Scholar 

  59. Rotaru, D., Wood, T.: MArBled circuits: mixing arithmetic and boolean circuits with active security. In: Hao, F., Ruj, S., Sen Gupta, S. (eds.) INDOCRYPT 2019. LNCS, vol. 11898, pp. 227–249. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35423-7_12

    Chapter  Google Scholar 

  60. Simić, S., Bemporad, A., Inverso, O., Tribastone, M.: Tight error analysis in fixed-point arithmetic. Form. Asp. Comput. 34(1) (2022). https://doi.org/10.1145/3524051

  61. Szabo, N.S., Tanaka, R.I.: Residue arithmetic and its applications to computer technology / Nicholas S. Szabo, Richard I. Tanaka. McGraw-Hill series in information processing and computers, McGraw-Hill, New York (1967)

    Google Scholar 

  62. Wagh, S., Gupta, D., Chandran, N.: SecureNN: 3-party secure computation for neural network training. PoPETs 2019(3), 26–49 (2019). https://doi.org/10.2478/popets-2019-0035

    Article  Google Scholar 

  63. Yao, A.C.C.: Protocols for secure computations (extended abstract). In: 23rd FOCS, pp. 160–164. IEEE Computer Society Press, November 1982. https://doi.org/10.1109/SFCS.1982.38

  64. Yuan, S., Shen, M., Mironov, I., Nascimento, A.C.A.: Practical, label private deep learning training based on secure multiparty computation and differential privacy. Cryptology ePrint Archive, Report 2021/835 (2021). https://eprint.iacr.org/2021/835

Download references

Author information

Authors and Affiliations

  1. Zama, Paris, France

    Tore Kasper Frederiksen

  2. Mysten Labs, Paris, France

    Jonas Lindstrøm

  3. The Alexandra Institute, Aarhus, Denmark

    Mikkel Wienberg Madsen & Anne Dorte Spangsberg

Authors
  1. Tore Kasper Frederiksen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonas Lindstrøm
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mikkel Wienberg Madsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anne Dorte Spangsberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mikkel Wienberg Madsen .

Editor information

Editors and Affiliations

  1. New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

    Christina Pöpper

  2. Radboud University Nijmegen, Nijmegen, The Netherlands

    Lejla Batina

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Frederiksen, T.K., Lindstrøm, J., Madsen, M.W., Spangsberg, A.D. (2024). A New Approach to Efficient and Secure Fixed-Point Computation. In: Pöpper, C., Batina, L. (eds) Applied Cryptography and Network Security. ACNS 2024. Lecture Notes in Computer Science, vol 14583. Springer, Cham. https://doi.org/10.1007/978-3-031-54770-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-54770-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54769-0

  • Online ISBN: 978-3-031-54770-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation