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Authors: Matús Harvan 1 ; Thomas Locher 2 ; Marta Mularczyk 3 and Yvonne Anne Pignolet 2

Affiliations: 1 Enovos, Luxembourg ; 2 ABB Corporate Research, Switzerland ; 3 ETH Zurich, Switzerland

Keyword(s): Machine Learning, (Linear) Regression, Cloud Computing, (Partially) Homomorphic Encryption.

Related Ontology Subjects/Areas/Topics: Applied Cryptography ; Cryptographic Techniques and Key Management ; Data and Application Security and Privacy ; Data Engineering ; Databases and Data Security ; Information and Systems Security ; Privacy ; Privacy Enhancing Technologies ; Security and Privacy in the Cloud

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, ou r algorithms outperform state-of-the-art schemes based on fully homomorphic encryption or multi-party computation by several orders of magnitude. (More)

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Paper citation in several formats:
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 (ICETE 2017) - SECRYPT; ISBN 978-989-758-259-2; ISSN 2184-3236, SciTePress, pages 255-266. DOI: 10.5220/0006400102550266

@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 (ICETE 2017) - SECRYPT},
year={2017},
pages={255-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006400102550266},
isbn={978-989-758-259-2},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017) - SECRYPT
TI - Privacy-preserving Regression on Partially Encrypted Data
SN - 978-989-758-259-2
IS - 2184-3236
AU - Harvan, M.
AU - Locher, T.
AU - Mularczyk, M.
AU - Pignolet, Y.
PY - 2017
SP - 255
EP - 266
DO - 10.5220/0006400102550266
PB - SciTePress