The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation
Open accessPublished version of a preprint
Abstract
BACKGROUND: Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis. OBJECTIVE: The goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression). METHODS: We adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie, reduce computation cost) and (2) new packing and parallelization techniques. RESULTS: Using real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took approximately 116 minutes to obtain the training model from the homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset. CONCLUSIONS: We present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still.
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“In contrast, under the LDP model, at least Ω( √ n/ϵ ) additive error in expectation must be incurred by any ϵ -DP mechanism for the same task [117], [111].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [117]
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Metadata
- Authors
- Kim, M., Song, Y., Wang, S., Xia, Y., & Jiang, X.
- Affiliations
- University of California San Diego (US)
- Seoul National University (KR)
- Princeton University (US)
- Corresponding author
- Kim, Miran
- Venue
- JMIR Medical Informatics
- Publisher
- JMIR Publications Inc.
- Published
- 2018-04-17
- Volume
- 6
- Issue
- 2
- Pages
- e19
- ISSN
- 2291-9694 (ISSN-L)
- PMC
- PMC5930176
- OpenAlex
- W2801958627
- Cited by
- 257
- References
- 15
- Citations by year
- 2026: 8 · 2025: 28 · 2024: 37 · 2023: 36 · 2022: 37 · 2021: 39 · 2020: 41 · 2019: 25 · 2018: 6
- FWCI
- 15.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Informatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IACR Cryptology ePrint Archive (submittedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- National Research Foundation
- National Research Foundation of Korea (2017R1A5A1015626)
- National Human Genome Research Institute (R00HG008175)
- National Institute of General Medical Sciences (R01GM118574, R01GM118609)
- National Institute of Biomedical Imaging and Bioengineering (U01EB023685)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- homomorphic encryption, privacy-preserving machine learning, secure data outsourcing, cloud security, real number computation, least squares approximation, data packing, parallelization
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