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Towards Real-World Private Computations with Homomorphic Encryption: Current Solutions and Open Challenges

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Computer Security. ESORICS 2023 International Workshops (ESORICS 2023)

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Abstract

There is an increasing need to share sensitive information within and beyond organisations. Protecting this information is vital for commercial and regulatory reasons. Homomorphic Encryption (HE) has come to the fore as a mechanism to enable the sharing of confidential data in a secure and private manner. Multiple open-source libraries are now publicly available, providing organisations with the tools to utilise the advantages of HE. While research devoted much effort to the academic and cryptographic aspects of HE schemes, research explicitly focusing on real-world financial applications is comparably rare. There is a need to provide a comparative analysis and related benchmarking of the most suitable HE libraries, having fixed the functional and non-functional requirements of the enterprise application of interest. We consider the motivation and background for HE and discuss the most promising open-source HE libraries. Having introduced real-world use cases in a financial context, we then illustrate outstanding challenges and how we plan to circumvent open points, introducing HELT (Homomorphic Encryption Libraries Toolkit).

The views and opinions expressed in this paper are those of the authors and do not necessarily reflect the official policy or position of Banca d’Italia.

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References

  1. Acar, A., Aksu, H., Uluagac, A.S., Conti, M.: A survey on homomorphic encryption schemes: theory and implementation. ACM Comput. Surv. (Csur) 51(4), 1–35 (2018)

    Article  Google Scholar 

  2. Aguilar Melchor, C., Kilijian, M.-O., Lefebvre, C., Ricosset, T.: A comparison of the homomorphic encryption libraries HElib, SEAL and FV-NFLlib. In: Lanet, J.-L., Toma, C. (eds.) SECITC 2018. LNCS, vol. 11359, pp. 425–442. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12942-2_32

  3. Albrecht, M., et al.: Homomorphic encryption security standard. Tech. rep., HomomorphicEncryption.org, Toronto, Canada (November 2018)

    Google Scholar 

  4. Applied Research Team: Blind learning environment. Tech. rep., Bank of Italy, Rome, Italy (June 2022). https://www.bankit.art/assets/downloads/BLE_Unrestricted.pdf (Accessed 21 June 2023)

  5. Armknecht, F., et al.: A guide to fully homomorphic encryption. Cryptology ePrint Archive (2015)

    Google Scholar 

  6. Badawi, A.A., et al.: Openfhe: Open-source fully homomorphic encryption library. Cryptology ePrint Archive, Paper 2022/915 (2022)

    Google Scholar 

  7. Bellomarini, L., Blasi, L., Laurendi, R., Sallinger, E.: Financial data exchange with statistical confidentiality: a reasoning-based approach. In: Proceedings of the 24th International Conference on Extending Database Technology, EDBT 2021, Nicosia, Cyprus, 23–26 March 2021, pp. 558–569 (2021)

    Google Scholar 

  8. Biasioli, B., Marcolla, C., Calderini, M., Mono, J.: Improving and automating bfv parameters selection: An average-case approach. Cryptology ePrint Archive, Paper 2023/600 (2023). https://eprint.iacr.org/2023/600

  9. Bos, J.W., Lauter, K., Naehrig, M.: Private predictive analysis on encrypted medical data. J. Biomed. Inform. 50, 234–243 (2014)

    Article  Google Scholar 

  10. Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: Tfhe: fast fully homomorphic encryption over the torus. J. Cryptol. 33(1), 34–91 (2020)

    Article  MathSciNet  Google Scholar 

  11. Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: TFHE: fast fully homomorphic encryption library (August 2016). https://tfhe.github.io/tfhe/

  12. Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds. Cryptology ePrint Archive, Paper 2016/870 (2016), https://eprint.iacr.org/2016/870

  13. Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-first annual ACM Symposium on Theory of Computing, pp. 169–178 (2009)

    Google Scholar 

  14. Gouert, C., Mouris, D., Tsoutsos, N.G.: Sok: New insights into fully homomorphic encryption libraries via standardized benchmarks. Cryptology ePrint Archive, Paper 2022/425 (2022)

    Google Scholar 

  15. Han, K., Hong, S., Cheon, J.H., Park, D.: Logistic regression on homomorphic encrypted data at scale. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9466–9471 (2019)

    Google Scholar 

  16. IBM: Ibm security homomorphic encryption services (2023). https://www.ibm.com/security/services/homomorphic-encryption, (Accessed 30 June 2023)

  17. Iezzi, M.: Practical privacy-preserving data science with homomorphic encryption: an overview. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 3979–3988. IEEE (2020)

    Google Scholar 

  18. Iezzi, M.: The evolving path of "the right to be left alone" - when privacy meets technology. In: 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 225–234 (2021)

    Google Scholar 

  19. Library, PHES: Library (2023). https://palisade-crypto.org/software-library/

  20. Lloyd, J.: Homomorphic encryption: the future of secure data sharing in finance? (2022). https://www.turing.ac.uk/blog/homomorphic-encryption-future-secure-data-sharing-finance (Accessed 30 June 2023)

  21. López-Alt, A., Tromer, E., Vaikuntanathan, V.: 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, pp. 1219–1234 (2012)

    Google Scholar 

  22. Marcolla, C., Sucasas, V., Manzano, M., Bassoli, R., Fitzek, F.H., Aaraj, N.: Survey on fully homomorphic encryption, theory and applications (2022)

    Google Scholar 

  23. Marrone, S., Tortora, A., Bellini, E., Maione, A., Raimondo, M.: Development of a testbed for fully homomorphic encryption solutions. In: 2021 IEEE International Conference on Cyber Security and Resilience (CSR), pp. 206–211 (2021)

    Google Scholar 

  24. Masters, O., et al.: Towards a homomorphic machine learning big data pipeline for the financial services sector. Cryptology ePrint Archive (2019)

    Google Scholar 

  25. Mono, J., Marcolla, C., Land, G., Güneysu, T., Aaraj, N.: Finding and evaluating parameters for bgv. Cryptology ePrint Archive (2022)

    Google Scholar 

  26. Mouchet, C.V., Bossuat, J.P., Troncoso-Pastoriza, J.R., Hubaux, J.P.: Lattigo: A multiparty homomorphic encryption library in go. In: Proceedings of the 8th Workshop on Encrypted Computing and Applied Homomorphic Cryptography, pp. 64–70. No. CONF (2020)

    Google Scholar 

  27. Sankar, L., Rajagopalan, S.R., Poor, H.V.: An information-theoretic approach to privacy. In: 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1220–1227. IEEE (2010)

    Google Scholar 

  28. Microsoft SEAL (release 4.1). https://github.com/Microsoft/SEAL (Jan 2023), microsoft Research, Redmond, WA

  29. Takeshita, J., Koirala, N., McKechney, C., Jung, T.: Heprofiler: an in-depth profiler of approximate homomorphic encryption libraries (2022)

    Google Scholar 

  30. Varia, M., Yakoubov, S., Yang, Y.: Hetest: A homomorphic encryption testing framework. In: Financial Cryptography Workshops (2015)

    Google Scholar 

  31. Viand, A., Jattke, P., Hithnawi, A.: Sok: fully homomorphic encryption compilers. In: 2021 IEEE Symposium on Security and Privacy (SP), pp. 1092–1108. IEEE (2021)

    Google Scholar 

  32. Wood, A., Najarian, K., Kahrobaei, D.: Homomorphic encryption for machine learning in medicine and bioinformatics. ACM Comput. Surv. (CSUR) 53(4), 1–35 (2020)

    Article  Google Scholar 

  33. Wood, A., Shpilrain, V., Najarian, K., Kahrobaei, D.: Private naive bayes classification of personal biomedical data: Application in cancer data analysis. Comput. Biol. Med. 105, 144–150 (2019)

    Article  Google Scholar 

  34. Zama (2022). https://www.zama.ai/post/introducing-the-concrete-framework (Accessed 30 June 2023)

  35. Zama: Library (2023). https://github.com/zama-ai

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Correspondence to Michela Iezzi .

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Iezzi, M., Maple, C., Leonetti, A. (2024). Towards Real-World Private Computations with Homomorphic Encryption: Current Solutions and Open Challenges. In: Katsikas, S., et al. Computer Security. ESORICS 2023 International Workshops. ESORICS 2023. Lecture Notes in Computer Science, vol 14398. Springer, Cham. https://doi.org/10.1007/978-3-031-54204-6_17

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  • DOI: https://doi.org/10.1007/978-3-031-54204-6_17

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