Abstract
In recent years, the trend has increasingly been to store and process data in the cloud. However, this is based on the premise that cloud providers treat the data in a trustworthy manner. One way of using the data in the cloud without the provider having access to it is homomorphic encryption. However, since this encryption has only recently become practicable, analysis of its for practical applications is still in its infancy. Therefore, we investigate the performance of homomorphic encryption using a real-world application, namely linear regression. Our main finding is that although the homomorphic computation of linear regression is in the range of minutes and thus slower than in the non-homomorphic case, linear regression can be computed homomorphic and is therefore suitable for use cases where data security is the top priority.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akavia, A., et al.: Linear-regression on packed encrypted data in the two-server model. In: Proceedings of the 7th ACM Workshop on Encrypted Computing & Applied Homomorphic Cryptography (2019)
Chen, B., et al.: Implementing linear regression with homomorphic encryption. Procedia Comput. Sci. 202, 324–329 (2022). International Conference on Identification, Information and Knowledge in the internet of Things, 2021
Cheon, J.H., Kim, A., Kim, M., Song, Y.: Homomorphic encryption for arithmetic of approximate numbers. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 409–437. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70694-8_15
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing (2009)
Gentry, C.: Computing arbitrary functions of encrypted data. Commun. ACM 53(3) (2010). https://doi.org/10.1145/1666420.1666444
Giacomelli, I., Jha, S., Joye, M., Page, C.D., Yoon, K.: Privacy-preserving ridge regression with only linearly-homomorphic encryption. In: Preneel, B., Vercauteren, F. (eds.) ACNS 2018. LNCS, vol. 10892, pp. 243–261. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93387-0_13
Goey, J.Z., et al.: Accelerating number theoretic transform in GPU platform for fully homomorphic encryption. J. Supercomput. 77(2), 1455–1474 (2021)
Hall, R., et al.: Secure multiple linear regression based on homomorphic encryption. J. Off. Stat. 27(4), 669 (2011)
Kaggle: House Prices - Advanced Regression Techniques. https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data. Accessed 29 Oct 2022
Kamara, S., Mohassel, P., Raykova, M.: Outsourcing multi-party computation. Cryptology ePrint Archive (2011)
Katz, J., et al.: Introduction to Modern Cryptography, 2nd edn. Chapman Hall, CRC Cryptography and Network Security, CRC Press, Boca Raton; London; New York (2015)
Kim, M., et al.: Secure logistic regression based on homomorphic encryption: Design and evaluation. JMIR Med. Inform. 6(2), e8805 (2018)
Ng, A.: CS229 lecture notes. CS229 Lect. Notes 1(1), 1–3 (2000). https://see.stanford.edu/materials/aimlcs229/cs229-notes1.pdf. Accessed 25 Sept 2022
PALISADE Project: Palisade Homomorphic Encryption Software Library. https://palisade-crypto.org/. Accessed 17 Jan 2023
Qiu, G., et al.: Privacy-preserving linear regression on distributed data by homomorphic encryption and data masking. IEEE Access 8, 107601–107613 (2020)
Rivest, R.L., et al.: On data banks and privacy homomorphisms. Found. Secure Comput. 4(11), 169–180 (1978)
Steve, R., Angus, L.: Oracle ceo hurd says 80% of corporate data centers gone by 2025. https://www.wsj.com/articles/BL-CIOB-11316. Accessed 25 Sept 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prantl, T. et al. (2023). Performance Impact Analysis of Homomorphic Encryption: A Case Study Using Linear Regression as an Example. In: Meng, W., Yan, Z., Piuri, V. (eds) Information Security Practice and Experience. ISPEC 2023. Lecture Notes in Computer Science, vol 14341. Springer, Singapore. https://doi.org/10.1007/978-981-99-7032-2_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-7032-2_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7031-5
Online ISBN: 978-981-99-7032-2
eBook Packages: Computer ScienceComputer Science (R0)