Skip to main content
SpringerLink
Log in

Efficient homomorphic encryption framework for privacy-preserving regression

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Homomorphic encryption (HE) has recently attracted considerable attention as a key solution for privacy-preserving machine learning because HE can apply to various areas that require to delegate outsourcing computations of user’s data. Nevertheless, its computational inefficiency still hinders its wider application. In this study, we propose an alternative to bridge the gap between the privacy and efficiency of HE by encrypting only a small amount of private information. We first derive an exact solution to HE-friendly ridge regression with multiple private variables, while linearly reducing the computational complexity of this algorithm over the number of variables. The proposed method has the advantage that it can be implemented using any HE scheme. Moreover, we propose an adversarial perturbation method that can prevent potential attacks on private variables, which have rarely been explored in HE-based machine learning studies. An extensive experiment on real-world benchmarking datasets supports the effectiveness of our method.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Algorithm 2
Fig. 3
Algorithm 3
Algorithm 4
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Acharya MS, Armaan A, Antony AS (2019) A comparison of regression models for prediction of graduate admissions. In: 2019 International conference on computational intelligence in data science (ICCIDS). IEEE, pp 1–5

  2. Aono Y, Hayashi T, Phong LT et al (2017) Input and output privacy-preserving linear regression. IEICE Trans Inf Syst 100(10):2339–2347

    Article  Google Scholar 

  3. Brakerski Z, Gentry C, Vaikuntanathan V (2014) (Leveled) fully homomorphic encryption without bootstrapping. ACM Trans Comput Theory (TOCT) 6(3):1–36

    Article  MathSciNet  MATH  Google Scholar 

  4. Byun J, Lee W, Lee J (2021) Parameter-free he-friendly logistic regression. Adv Neural Inf Process Syst 34:8457–8468

    Google Scholar 

  5. Cai Z, He Z, Guan X et al (2016) Collective data-sanitization for preventing sensitive information inference attacks in social networks. IEEE Trans Dependable Secure Comput 15(4):577– 590

    Google Scholar 

  6. Cheon JH, Kim A, Kim M et al (2017) Homomorphic encryption for arithmetic of approximate numbers. In: International conference on the theory and application of cryptology and information security. Springer, pp 409–437

  7. De Spiegeleer J, Madan DB, Reyners S et al (2018) Machine learning for quantitative finance: fast derivative pricing, hedging and fitting. Quantitative Finance 18(10):1635–1643

    Article  MathSciNet  MATH  Google Scholar 

  8. Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml

  9. Feldman M, Friedler SA, Moeller J et al (2015) Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 259–268

  10. Fredrikson M, Jha S, Ristenpart T (2015) Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp 1322–1333

  11. Gai K, Qiu M, Zhao H (2017) Privacy-preserving data encryption strategy for big data in mobile cloud computing. IEEE Trans Big Data

  12. Gentry C (2009) Fully homomorphic encryption using ideal lattices. In: Proceedings of the forty-first annual ACM symposium on theory of computing, pp 169–178

  13. Gentry C, Halevi S, Smart NP (2012) Fully homomorphic encryption with polylog overhead. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 465–482

  14. Guo L, Ying X, Wu X (2010) On attribute disclosure in randomization based privacy preserving data publishing. In: 2010 IEEE international conference on data mining workshops. IEEE, pp 466–473

  15. Halevi S (2017) Homomorphic encryption. Tutorials on the foundations of cryptography. isc

  16. Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. In: Advances in neural information processing systems, pp 3315–3323

  17. Janai J, Güney F, Behl A et al (2020) Computer vision for autonomous vehicles: problems, datasets and state of the art. Foundations and Trends®;, in Computer Graphics and Vision 12(1–3):1–308

    Article  Google Scholar 

  18. Jia J, Gong NZ (2018) Attriguard: a practical defense against attribute inference attacks via adversarial machine learning. In: 27th {USENIX} security symposium ({USENIX} security 18), pp 513–529

  19. Kaissis GA, Makowski MR, Rückert D et al (2020) Secure, privacy-preserving and federated machine learning in medical imaging. Nature Mach Intell:1–7

  20. Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc National Acad Sci 110(15):5802–5805

    Article  Google Scholar 

  21. Lee W, Ko H, Byun J et al (2021) Fair clustering with fair correspondence distribution. Inf Sci 581:155–178

    Article  MathSciNet  Google Scholar 

  22. Liu Q, Shen H, Sang Y (2014) A privacy-preserving data publishing method for multiple numerical sensitive attributes via clustering and multi-sensitive bucketization. In: 2014 Sixth international symposium on parallel architectures, algorithms and programming. ieee, pp 220–223

  23. Lou Q, Feng B, Fox GC et al (2019) Glyph: fast and accurately training deep neural networks on encrypted data. arXiv:191107101

  24. Ma L, Sun B (2020) Machine learning and ai in marketing–connecting computing power to human insights. Int J Res Mark 37(3):481–504

    Article  Google Scholar 

  25. Morshed T, Alhadidi D, Mohammed N (2018) Parallel linear regression on encrypted data. In: 2018 16th Annual conference on privacy, security and trust (PST). IEEE, pp 1–5

  26. Park S, Byun J, Lee J et al (2020) He-friendly algorithm for privacy-preserving svm training. IEEE Access 8:57,414–57,425

    Article  Google Scholar 

  27. Park S, Byun J, Lee J (2022) Privacy-preserving fair learning of support vector machine with homomorphic encryption. In: Proceedings of the ACM Web Conference 2022, pp 3572–3583

  28. Poursaeed O, Katsman I, Gao B et al (2018) Generative adversarial perturbations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4422–4431

  29. Qiu G, Gui X, Zhao Y (2020) Privacy-preserving linear regression on distributed data by homomorphic encryption and data masking. IEEE Access 8:107,601–107,613

    Article  Google Scholar 

  30. Sumathi M, Sangeetha S (2018) Enhanced elliptic curve cryptographic technique for protecting sensitive attributes in cloud storage. In: 2018 IEEE international conference on computational intelligence and computing research (ICCIC). IEEE, pp 1–5

  31. Voigt P, Von Dem Bussche A (2017) The Eu General Data Protection Regulation (gdpr). A Practical Guide 1st Edn. Springer International Publishing, Cham

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2019R1A2C2002358) and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) Grant funded by the Korean Government (MSIT) (No. 2022-0-00984).

Author information

Authors and Affiliations

  1. Industrial Engineering, Seoul National University, Seoul, 08826, Republic of Korea

    Junyoung Byun, Yujin Choi & Jaewook Lee

  2. Department of Convergence Security Engineering, Sungshin Women’s University, Seoul, 02844, Republic of Korea

    Saerom Park

Authors
  1. Junyoung Byun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saerom Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yujin Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jaewook Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaewook Lee.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Byun, J., Park, S., Choi, Y. et al. Efficient homomorphic encryption framework for privacy-preserving regression. Appl Intell 53, 10114–10129 (2023). https://doi.org/10.1007/s10489-022-04015-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04015-z

Keywords

Search

Navigation