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Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation
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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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Cites
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Self-citationLogistic regression and Artificial Neural Network Classification models: A methodology review
Dreiseitl, S., & Ohno-Machado, L. (2002) · Journal of Biomedical Informatics · doi:10.1016/s1532-0464(03)00034-0 · Cited by 2,177
Moltu, C., Stefansen, J., Svisdahl, M., & Veseth, M. (2012) · Disability and Rehabilitation · doi:10.3109/09638288.2012.656792 · Cited by 1,247
Historical Development of the Newton–Raphson Method
Ypma, T. J. (1995) · SIAM Review · doi:10.1137/1037125 · Cited by 956
Prognostic Modeling with Logistic Regression Analysis
Steyerberg, E. W., Eijkemans, M. J. C., Harrell, F. E., & Habbema, J. D. F. (2001) · Medical Decision Making · doi:10.1177/0272989x0102100106 · Cited by 559
Private predictive analysis on encrypted medical data
Bos, J. W., Lauter, K., & Naehrig, M. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.04.003 · Cited by 283 · cited 4×
“TechniquesProblemReference Homomorphic encryptionLinear means classifier/discriminative analysisGraepel et al [11] Homomorphic encryptionPrediction using learned logistic regression modelBos et al [14] Homomorphic encryptionPrediction using learned neural networksDowlin et al [15] Additive homomorphic encryptionLogist…”
MethodInfluentialScalable and Secure Logistic Regression via Homomorphic Encryption
Aono, Y., Hayashi, T., Phong, L. T., & Wang, L. (2016) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/2857705.2857731 · Cited by 183
Grid Binary LOgistic REgression (GLORE): building shared models without sharing data
Wu, Y. S., Jiang, X., Kim, J., & Lucila Ohno-machado, A. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000862 · Cited by 171
Self-citationKennedy, R. L., Fraser, H., McStay, L., & Harrison, R. F. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a015035 · Cited by 86
Flagged first, then by citations
Research works printed but not linked: 7
- SkinnerEOcto BarnettGSingerDMulleyALewRSticklerSMorganMThibaultGThe use of logistic regression in diagnostic and prognostic prediction in a medical intensive care unitProc Annu Symp Comput Appl Med Care19801105Proceedings of the Annual Symposium on Computer Application in Medical Care American Medical Informatics Association; ;1Nov 2-5, 1980Washington, DC222227
- GraepelTLauterKNaehrigMML confidential: machine learning on encrypted dataLecture Notes in Computer Science, vol 78392012International Conference on Information Security and CryptologyNov 28-30, 2012SeoulBerlin, HeidelbergSpringer121
- DowlinNGilad-BachrachRLaineKLauterKNaehrigMWernsingJCryptoNets: applying neural networks to encrypted data with high throughput and accuracyProceedings of the 33rd International Conference on International Conference on Machine Learning201633rd International Conference on Machine LearningJun 19-24, 2016New York201210
- MohasselPZhangYSecureML: a system for scalable privacy-preserving machine learning20172017 IEEE Symposium on Security and PrivacyMay 22-26, 2017San Jose, CA1938
- CheonJKimAKimMSongYHomomorphic encryption for arithmetic of approximate numbers201723rd International Conference on the Theory and Applications of Cryptology and Information SecurityDec 3-7, 2017Hong Kong409437
- BottouLLarge-scale machine learning with stochastic gradient descentProceedings of the 19th International Conference on Computational Statistics201019th International Conference on Computational StatisticsAug 22-27, 2010Paris177186
- CheonJKimMKimMSearch-and-compute on encrypted dataInternational Conference on Financial Cryptography and Data Security2015FC 2015 International WorkshopsJan 30, 2015San Juan, Puerto Rico142159
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Mandal, K., & Gong, G. (2019) · Cloud Computing Security Workshop · doi:10.1145/3338466.3358926 · Cited by 93
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Self-citation
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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