Privacy-preserving Regression on Partially Encrypted Data

Matús Harvan, Thomas Locher, Marta Mularczyk, Yvonne Anne Pignolet

2017

Abstract

There is a growing interest in leveraging the computational resources and storage capacities of remote compute and storage infrastructures for data analysis. However, the loss of control over the data raises concerns about data privacy. In order to remedy these concerns, data can be encrypted before transmission to the remote infrastructure, but the use of encryption renders data analysis a challenging task. An important observation is that it suffices to encrypt only certain parts of the data in various real-world scenarios, which makes it possible to devise efficient algorithms for secure remote data analysis based on partially homomorphic encryption. We present several computationally efficient algorithms for regression analysis, focusing on linear regression, that work with partially encrypted data. Our evaluation shows that we can both train models and compute predictions with these models quickly enough for practical use. At the expense of full data confidentiality, our algorithms outperform state-of-the-art schemes based on fully homomorphic encryption or multi-party computation by several orders of magnitude.

Paper Citation

in Harvard Style

Harvan M., Locher T., Mularczyk M. and Pignolet Y. (2017). Privacy-preserving Regression on Partially Encrypted Data . In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 6: SECRYPT, (ICETE 2017) ISBN 978-989-758-259-2, pages 255-266. DOI: 10.5220/0006400102550266

in Bibtex Style

@conference{secrypt17,
author={Matús Harvan and Thomas Locher and Marta Mularczyk and Yvonne Anne Pignolet},
title={Privacy-preserving Regression on Partially Encrypted Data},
booktitle={Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 6: SECRYPT, (ICETE 2017)},
year={2017},
pages={255-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006400102550266},
isbn={978-989-758-259-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications - Volume 6: SECRYPT, (ICETE 2017)
TI - Privacy-preserving Regression on Partially Encrypted Data
SN - 978-989-758-259-2
AU - Harvan M.
AU - Locher T.
AU - Mularczyk M.
AU - Pignolet Y.
PY - 2017
SP - 255
EP - 266
DO - 10.5220/0006400102550266