poster

Scalable and Secure Logistic Regression via Homomorphic Encryption

Authors:

Yoshinori Aono,

Takuya Hayashi,

Le Trieu Phong,

Lihua WangAuthors Info & Claims

CODASPY '16: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy

Pages 142 - 144

https://doi.org/10.1145/2857705.2857731

Published: 09 March 2016 Publication History

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Abstract

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.

References

[1]

Y. Aono, T. Hayashi, L. T. Phong, L. Wang. Scalable and Secure Logistic Regression via Homomorphic Encryption. IACR Cryptology ePrint Archive, 2016.

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[2]

J. W. Bos, K. E. Lauter, and M. Naehrig. Private predictive analysis on encrypted medical data. Journal of Biomedical Informatics, 50:234--243, 2014.

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[3]

M. Naehrig, K. E. Lauter, and V. Vaikuntanathan. Can homomorphic encryption be practical? CCSW 2011, pages 113--124. ACM, 2011.

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R. L. Rivest, L. Adleman, and M. L. Dertouzos. On data banks and privacy homomorphisms. Foundations of secure computation, 4(11):169--180, 1978.

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J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett. Functional mechanism: Regression analysis under differential privacy. PVLDB, 5(11):1364--1375, 2012.

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Shen CZhang WZhou TZhang L(2024)A Security-Enhanced Federated Learning Scheme Based on Homomorphic Encryption and Secret SharingMathematics10.3390/math1213199312:13(1993)Online publication date: 27-Jun-2024
https://doi.org/10.3390/math12131993
Lyu LYu HMa XChen CSun LZhao JYang QYu P(2024)Privacy and Robustness in Federated Learning: Attacks and DefensesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321698135:7(8726-8746)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2022.3216981
Lu XSami HGüler B(2024)Privacy-Preserving Collaborative Learning With Linear Communication ComplexityIEEE Transactions on Information Theory10.1109/TIT.2023.334527070:8(5857-5887)Online publication date: Aug-2024
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Index Terms

Scalable and Secure Logistic Regression via Homomorphic Encryption
1. Security and privacy
  1. Cryptography
    1. Public key (asymmetric) techniques
      1. Public key encryption
  2. Network security
    1. Security protocols

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Information

Published In

CODASPY '16: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy

March 2016

340 pages

ISBN:9781450339353

DOI:10.1145/2857705

General Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Sandhu
University of Texas at San Antonio, USA
,
Program Chair:
Alexander Pretschner
Technische Universität München, Germany

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 09 March 2016

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SIGSAC

CODASPY'16: Sixth ACM Conference on Data and Application Security and Privacy

March 9 - 11, 2016

Louisiana, New Orleans, USA

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CODASPY '16 Paper Acceptance Rate 22 of 115 submissions, 19%;

Overall Acceptance Rate 149 of 789 submissions, 19%

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Shen CZhang WZhou TZhang L(2024)A Security-Enhanced Federated Learning Scheme Based on Homomorphic Encryption and Secret SharingMathematics10.3390/math1213199312:13(1993)Online publication date: 27-Jun-2024
https://doi.org/10.3390/math12131993
Lyu LYu HMa XChen CSun LZhao JYang QYu P(2024)Privacy and Robustness in Federated Learning: Attacks and DefensesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321698135:7(8726-8746)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2022.3216981
Lu XSami HGüler B(2024)Privacy-Preserving Collaborative Learning With Linear Communication ComplexityIEEE Transactions on Information Theory10.1109/TIT.2023.334527070:8(5857-5887)Online publication date: Aug-2024
https://doi.org/10.1109/TIT.2023.3345270
Hallaji ERazavi-Far RSaif MWang BYang Q(2024)Decentralized Federated Learning: A Survey on Security and PrivacyIEEE Transactions on Big Data10.1109/TBDATA.2024.336219110:2(194-213)Online publication date: Apr-2024
https://doi.org/10.1109/TBDATA.2024.3362191
Geng JMou YLi QLi FBeyan ODecker SRong C(2024)Improved Gradient Inversion Attacks and Defenses in Federated LearningIEEE Transactions on Big Data10.1109/TBDATA.2023.323911610:6(839-850)Online publication date: Dec-2024
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Pulido-Gaytan BTchemykh ABabenko MCortés-Mendoza JGonzález-Vélez HAvetisyan A(2024)Enhancing Cloud Security through Efficient Polynomial Approximations for Homomorphic Evaluation of Neural Network Activation Functions2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)10.1109/CCGridW63211.2024.00011(42-49)Online publication date: 6-May-2024
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Heo JYoo JSong BYoon J(2024)Accelerating Secure Permutation: Application to Matrix AlgebraIEEE Access10.1109/ACCESS.2024.352240012(198156-198166)Online publication date: 2024
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Chen LXiao DYu ZZhang M(2024)Secure and efficient federated learning via novel multi-party computation and compressed sensingInformation Sciences: an International Journal10.1016/j.ins.2024.120481667:COnline publication date: 1-May-2024
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Analysis of Gong et al.'s CCA2-secure homomorphic encryption

On CCA-Secure somewhat homomorphic encryption

Improved anonymous proxy re-encryption with CCA security

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