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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
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 · see citing works
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 · see citing works
Moltu, C., Stefansen, J., Svisdahl, M., & Veseth, M. (2012) · Disability and Rehabilitation · doi:10.3109/09638288.2012.656792 · Cited by 1,247 · see citing works
Historical Development of the Newton–Raphson Method
Ypma, T. J. (1995) · SIAM Review · doi:10.1137/1037125 · Cited by 956 · see citing works
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 · see citing works
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× · see citing works
“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 · see citing works
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 · see citing works
Self-citationKennedy, R. L., Fraser, H., McStay, L., & Harrison, R. F. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a015035 · Cited by 86 · see citing works
Wang, S., Jiang, X., Wu, Y. S., Cui, L., Cheng, S. S. M., & Lucila Ohno-machado, A. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.03.008 · Cited by 74 · see citing works
Self-citationOptimized Search-and-Compute Circuits and Their Application to Query Evaluation on Encrypted Data
Cheon, J. H., Kim, M., & Kim, M. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2483486 · Cited by 58 · see citing works
Self-citationWebGLORE: a Web service for Grid LOgistic REgression
Jiang, W., Li, P., Wang, S., Wu, Y. S., Xue, M., Lucila Ohno-machado, A., & Jiang, X. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt559 · Cited by 38 · see citing works
Self-citationThe Definitive Guide to Complying with the HIPAA/HITECH Privacy and Security Rules
Trinckes, J. J. (2012) · Auerbach Publications eBooks · doi:10.1201/b13693 · Cited by 14 · see citing works
Application of Logistic Regression and Logistic Discrimination in Medical Decision Making
Dietz, E. J. (1987) · Biometrical Journal · doi:10.1002/bimj.4710290614 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- [7]Skinner E Octo Barnett G Singer D Mulley A Lew R Stickler S Morgan M Thibault G The use of logistic regression in diagnostic and prognostic prediction in a medical intensive care unit Proc Annu Symp Comput Appl Med Care 1980 11 05 Proceedings of the Annual Symposium on Computer Application in Medical Care American Medical Informatics Association;;1 Nov 2-5, 1980 Washington, DC 222 227
- [11]Graepel T Lauter K Naehrig M ML confidential: machine learning on encrypted data Lecture Notes in Computer Science, vol 7839 2012 International Conference on Information Security and Cryptology Nov 28-30, 2012 Seoul Berlin, Heidelberg Springer 1 21
- [15]Dowlin N Gilad-Bachrach R Laine K Lauter K Naehrig M Wernsing J CryptoNets: applying neural networks to encrypted data with high throughput and accuracy Proceedings of the 33rd International Conference on International Conference on Machine Learning 2016 33rd International Conference on Machine Learning Jun 19-24, 2016 New York 201 210
- [17]Mohassel P Zhang Y SecureML: a system for scalable privacy-preserving machine learning 2017 2017 IEEE Symposium on Security and Privacy May 22-26, 2017 San Jose, CA 19 38
- [18]Cheon J Kim A Kim M Song Y Homomorphic encryption for arithmetic of approximate numbers 2017 23rd International Conference on the Theory and Applications of Cryptology and Information Security Dec 3-7, 2017 Hong Kong 409 437
- [20]Bottou L Large-scale machine learning with stochastic gradient descent Proceedings of the 19th International Conference on Computational Statistics 2010 19th International Conference on Computational Statistics Aug 22-27, 2010 Paris 177 186
- [21]Halevi S Shoup V Algorithms in HElib Advances in Cryptology – CRYPTO 2014 2014 34th Annual Cryptology Conference Aug 17-21, 2014 Santa Barbara, CA 554 571
- [22]Cheon J Kim M Kim M Search-and-compute on encrypted data International Conference on Financial Cryptography and Data Security 2015 FC 2015 International Workshops Jan 30, 2015 San Juan, Puerto Rico 142 159
Cited by
Sorted by citations
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · see citing works
A secure federated transfer learning framework
Liu, Y., Kang, Y., Xing, C., Chen, T., & Yang, Q. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988525 · Cited by 645 · see citing works
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548 · see citing works
Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., & Yu, H. (2019) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00960ed2v01y201910aim043 · Cited by 397 · see citing works
Bootstrapping for Approximate Homomorphic Encryption
Cheon, J. H., Han, K., Kim, A., Kim, M., & Song, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78381-9_14 · Cited by 385 · see citing works
Self-citationA Full RNS Variant of Approximate Homomorphic Encryption
Cheon, J. H., Han, K., Kim, A., Kim, M., & Song, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10970-7_16 · Cited by 376 · see citing works
Self-citationMachine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321 · see citing works
Secure Outsourced Matrix Computation and Application to Neural Networks
Jiang, X., Kim, M., Lauter, K., & Song, Y. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243837 · Cited by 320 · see citing works
Self-citationF1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption
Samardzic, N., Feldmann, A., Krastev, A., Devadas, S., Dreslinski, R., Peikert, C., & Sánchez, D. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480070 · Cited by 296 · see citing works
Logistic regression model training based on the approximate homomorphic encryption
Kim, A., Song, Y., Kim, M., Lee, K., & Cheon, J. H. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0401-7 · Cited by 203 · see citing works
Self-citationBetter Bootstrapping for Approximate Homomorphic Encryption
Han, K., & Ki, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40186-3_16 · Cited by 201 · see citing works
Xie, Q., Jiang, S., Jiang, L., Huang, Y., Zhao, Z., Khan, S., Dai, W., Liu, Z., & Wu, K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3382875 · Cited by 209 · see citing works
Decentralized Federated Learning: A Survey on Security and Privacy
Hallaji, E., Razavi‐Far, R., Saif, M., Wang, B., & Yang, Q. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3362191 · Cited by 191 · see citing works
POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sav, S., Pyrgelis, A., Troncoso-Pastoriza, J. R., Froelicher, D., Bossuat, J.-P., Sousa, J. S., & Hubaux, J.-P. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24119 · Cited by 162 · see citing works
Towards Deep Neural Network Training on Encrypted Data
Nandakumar, K., Ratha, N., Pankanti, S., & Halevi, S. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00011 · Cited by 144 · see citing works
Liu, Y., Liu, Y., Liu, Z., Liang, Y., Meng, C., Zhang, J., & Zheng, Y. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2020.2992755 · Cited by 141 · see citing works
Logistic Regression on Homomorphic Encrypted Data at Scale
Han, K., Hong, S., Cheon, J. H., & Park, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019466 · Cited by 140 · see citing works
Li, J., Kuang, X., Lin, S., Ma, X., & Tang, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.03.041 · Cited by 139 · see citing works
Numerical Method for Comparison on Homomorphically Encrypted Numbers
Cheon, J. H., Kim, D., Kim, D., Lee, H. H., & Lee, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34621-8_15 · Cited by 133 · see citing works
Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme
Badawi, A. A., Polyakov, Y., Aung, K. M. M., Veeravalli, B., & Rohloff, K. R. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2902799 · Cited by 126 · see citing works
Privacy-Preserving Artificial Intelligence Techniques in Biomedicine
Torkzadehmahani, R., Nasirigerdeh, R., Blumenthal, D. B., Kacprowski, T., List, M., Matschinske, J., Späth, J., Wenke, N. K., & Baumbach, J. (2022) · Methods of Information in Medicine · doi:10.1055/s-0041-1740630 · Cited by 117 · see citing works
Federated learning: Applications, challenges and future directions
Bharati, S., Mondal, M. R. H., Podder, P., & Prasath, V. B. S. (2022) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-220006 · Cited by 117 · see citing works
Classification of Date Fruits into Genetic Varieties Using Image Analysis
Köklü, M., Kursun, R., Taşpınar, Y. S., & Çınar, İ. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/4793293 · Cited by 101 · see citing works
PrivFT: Private and Fast Text Classification With Homomorphic Encryption
Badawi, A. A., Hoang, L., Mun, C. F., Laine, K., & Aung, K. M. M. (2020) · IEEE Access · doi:10.1109/access.2020.3045465 · Cited by 96 · see citing works
Mandal, K., & Gong, G. (2019) · Cloud Computing Security Workshop · doi:10.1145/3338466.3358926 · Cited by 91 · see citing works
Federated Learning for Electronic Health Records
Dang, T. K., Xiang, L., Weng, J., & Feng, M. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3514500 · Cited by 90 · see citing works
Privacy-preserving logistic regression training
Bonte, C., & Vercauteren, F. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0398-y · Cited by 85 · see citing works
A Differentially Private Federated Learning Model against Poisoning Attacks in Edge Computing
Zhou, J., Wu, N., Wang, Y., Gu, S., Cao, Z., Dong, X., & Choo, K. R. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3168556 · Cited by 83 · see citing works
Label Leakage and Protection in Two-party Split LearningPreprint
Li, O., Sun, J., Xin, Y., Gao, W., Zhang, H., Xie, J., Smith, V., & Wang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08504 · Cited by 83 · see citing works
Scalable Privacy-Preserving Distributed Learning
Froelicher, D., Troncoso-Pastoriza, J. R., Pyrgelis, A., Sav, S., Sousa, J. S., Bossuat, J.-P., & Hubaux, J. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2021-0030 · Cited by 78 · see citing works
Homomorphic Encryption and Network Coding in IoT Architectures: Advantages and Future Challenges
Peralta, G., Cid-Fuentes, R. G., Bilbao, J., & Crespo, P. M. (2019) · Electronics · doi:10.3390/electronics8080827 · Cited by 70 · see citing works
Secure and Differentially Private Logistic Regression for Horizontally Distributed Data
Kim, M., Lee, J., Lucila Ohno-machado, A., & Jiang, X. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2925496 · Cited by 69 · see citing works
Self-citationSecure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Zhang, Q., Gu, B., Deng, C. D., & Huang, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17301 · Cited by 68 · see citing works
Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption
Cheon, J. H., Kim, D., Kim, Y., & Song, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2866697 · Cited by 67 · see citing works
Self-citationArazzi, M., Nicolazzo, S., & Nocera, A. (2023) · Information Systems Frontiers · doi:10.1007/s10796-023-10443-0 · Cited by 69 · see citing works
Blika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 69 · see citing works
Approximate homomorphic encryption based privacy-preserving machine learning: a survey
Yuan, J., Liu, W., Shi, J., & Li, Q. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11076-8 · Cited by 69 · see citing works
Unsupervised Machine Learning on Encrypted Data
Jäschke, A., & Armknecht, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10970-7_21 · Cited by 60 · see citing works
Secure and robust machine learning for healthcare: A survey
Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3013489 · Cited by 59 · see citing works
Froelicher, D., Troncoso-Pastoriza, J. R., Sousa, J. S., & Hubaux, J.-P. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2976612 · Cited by 59 · see citing works
Security of Cloud-Based Medical Internet of Things (MIoTs)
Gaurav, A., Psannis, K. E., & Peraković, D. (2021) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.285593 · Cited by 59 · see citing works
Optimized homomorphic encryption solution for secure genome-wide association studies
Blatt, M., Gusev, A., Polyakov, Y., Rohloff, K. R., & Vaikuntanathan, V. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0719-9 · Cited by 54 · see citing works
Improved Gradient Inversion Attacks and Defenses in Federated Learning
Geng, J., Mou, Y., Li, Q., Li, F., Beyan, O., Decker, S., & Rong, C. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3239116 · Cited by 53 · see citing works
Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview
Iezzi, M. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377989 · Cited by 53 · see citing works
Large-scale Secure XGB for Vertical Federated Learning
Fang, W., Zhao, D., Tan, J., Chen, C., Yu, C., Wang, L., Wang, L., Zhou, J., & Zhang, B. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482361 · Cited by 51 · see citing works
Privacy-Preserving Decision Trees Training and Prediction
Akavia, A., Leibovich, M., Resheff, Y. S., Ron, R., Shahar, M., & Vald, M. (2022) · ACM Transactions on Privacy and Security · doi:10.1145/3517197 · Cited by 50 · see citing works
In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare
Topaloglu, M. Y., Morrell, E. M., Rajendran, S., & Topaloĝlu, Ü. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.746497 · Cited by 50 · see citing works
Efficient Sorting of Homomorphic Encrypted Data With k -Way Sorting Network
Hong, S., Kim, S., Choi, J., Lee, Y., & Cheon, J. H. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3106167 · Cited by 46 · see citing works
A privacy-preserving logistic regression-based diagnosis scheme for digital healthcare
Zhou, Y., Song, L., Liu, Y., Vijayakumar, P., Gupta, B. B., Alhalabi, W., & Alsharif, H. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.022 · Cited by 46 · see citing works
SoK: New Insights into Fully Homomorphic Encryption Libraries via Standardized Benchmarks
Gouert, C., Mouris, D., & Tsoutsos, N. G. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0075 · Cited by 47 · see citing works
Sparse Logistic Regression-Based One-Bit SAR Imaging
Ge, S., Feng, D., Song, S., Wang, J., & Huang, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3322554 · Cited by 44 · see citing works
Semi-Parallel logistic regression for GWAS on encrypted data
Kim, M., Song, Y., Li, B., & Micciancio, D. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0724-z · Cited by 44 · see citing works
Self-citationSine Series Approximation of the Mod Function for Bootstrapping of Approximate HE
Jutla, C. S., & Manohar, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06944-4_17 · Cited by 41 · see citing works
Collaborative Machine Learning: Schemes, Robustness, and Privacy
Wang, J., Pal, A., Yang, Q., Kant, K., Zhu, K., & Guo, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3169347 · Cited by 42 · see citing works
A review on client-server attacks and defenses in federated learning
Sharma, A., & Marchang, N. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103801 · Cited by 44 · see citing works
Secure collaborative few-shot learning
Xie, Y., Wang, H., Yu, B., & Zhang, C. B. C. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106157 · Cited by 40 · see citing works
SEAL-Embedded: A Homomorphic Encryption Library for the Internet of Things
Natarajan, D., & Dai, W. (2021) · IACR Transactions on Cryptographic Hardware and Embedded Systems · doi:10.46586/tches.v2021.i3.756-779 · Cited by 40 · see citing works
Privacy-preserving approximate GWAS computation based on homomorphic encryption
Kim, D., Son, Y., Kim, D., Kim, A., Hong, S., & Cheon, J. H. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0722-1 · Cited by 39 · see citing works
Privacy-preserving federated neural network learning for disease-associated cell classification
Sav, S., Bossuat, J.-P., Troncoso-Pastoriza, J. R., Claassen, M., & Hubaux, J. (2022) · Patterns · doi:10.1016/j.patter.2022.100487 · Cited by 42 · see citing works
Nasirigerdeh, R., Torkzadehmahani, R., Matschinske, J., Frisch, T., List, M., Späth, J., Weiß, S., Völker, U., Pitkänen, E., Heider, D., et al. (2022) · Genome biology · doi:10.1186/s13059-021-02562-1 · Cited by 56 · see citing works
MAS-Encryption and its Applications in Privacy-Preserving Classifiers
Gao, C.-Z., Li, J., Xia, S., Choo, K. R., Lou, W., & Dong, C. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3009221 · Cited by 37 · see citing works
Homomorphic Training of 30,000 Logistic Regression Models
Bergamaschi, F. E., Halevi, S., Halevi, T., & Hunt, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21568-2_29 · Cited by 36 · see citing works
Achieving GWAS with homomorphic encryption
Sim, J. J., Mun, C. F., Chen, S., Tan, B. H. M., & Aung, K. M. M. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0717-y · Cited by 34 · see citing works
Manh, B. D., Nguyen, C.-H., Hoang, D. T., Nguyen, D. N., Zeng, M., & Pham, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3535792 · Cited by 37 · see citing works
Kang, M., & Lemieux, V. L. (2021) · Ledger · doi:10.5195/ledger.2021.239 · Cited by 33 · see citing works
Privacy-preserving construction of generalized linear mixed model for biomedical computation
Zhu, R., Jiang, C., Wang, X., Wang, S., Zheng, H., & Tang, H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa478 · Cited by 32 · see citing works
Self-citationSecure tumor classification by shallow neural network using homomorphic encryption
Hong, S., Park, J. H., Cho, W., Choe, H., & Cheon, J. H. (2022) · BMC Genomics · doi:10.1186/s12864-022-08469-w · Cited by 32 · see citing works
Curtis, B. R., & Player, R. (2019) · doi:10.1145/3338469.3358940 · Cited by 31 · see citing works
Lee, C. H., Lim, K. H., & Eswaran, S. (2025) · Discover Public Health · doi:10.1186/s12982-025-00505-w · Cited by 33 · see citing works
Sphinx: Enabling Privacy-Preserving Online Learning over the Cloud
Han, T., Zeng, C., Ren, Z., Chai, D., Zhang, J., Chen, K., & Yang, Q. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833648 · Cited by 28 · see citing works
Towards a Practical Cluster Analysis over Encrypted Data
Cheon, J. H., Kim, D., & Park, J. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-38471-5_10 · Cited by 27 · see citing works
Support vector machine classification over encrypted data
Huang, H., Wang, Y., & Zong, H. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02727-2 · Cited by 26 · see citing works
Near-Optimal Polynomial for Modulus Reduction Using L2-Norm for Approximate Homomorphic Encryption
Lee, Y., Lee, J.-W., Kim, Y.-S., & No, J. (2020) · IEEE Access · doi:10.1109/access.2020.3014369 · Cited by 26 · see citing works
Remark on the Security of CKKS Scheme in Practice.Preprint
Cheon, J. H., Hong, S., & Kim, D. (2020) · IACR Cryptology ePrint Archive · Cited by 26 · see citing works
Privacy-preserving logistic regression with secret sharing
Ghavamipour, A. R., Türkmen, F., & Jiang, X. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01811-y · Cited by 25 · see citing works
Self-citationJatain, D., Sıngh, V., & Dahiya, N. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.05.016 · Cited by 25 · see citing works
Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning.Preprint
Yaghini, M., Kulynych, B., & Troncoso, C. (2019) · arXiv (Cornell University) · Cited by 25 · see citing works
Zheng, Y., Xu, S., Wang, S., Gao, Y., & Hua, Z. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3279839 · Cited by 24 · see citing works
Self-citationFederated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
Hallaji, E., Razavi‐Far, R., & Saif, M. (2022) · Adaptation, learning, and optimization · doi:10.1007/978-3-031-11748-0_3 · Cited by 22 · see citing works
Towards a Homomorphic Machine Learning Big Data Pipeline for the Financial Services Sector.Preprint
Masters, O., Hunt, H., Steffinlongo, E., Crawford, J., & Bergamaschi, F. (2019) · IACR Cryptology ePrint Archive · Cited by 22 · see citing works
Nasirigerdeh, R., Torkzadehmahani, R., Matschinske, J., Frisch, T., List, M., Späth, J., Weiß, S., Völker, U., Heider, D., Wenke, N. K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.05.136382 · Cited by 22 · see citing works
Principal Component Analysis Using CKKS Homomorphic Scheme
Panda, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78086-9_4 · Cited by 21 · see citing works
Hybrid Differentially Private Federated Learning on Vertically Partitioned DataPreprint
Wang, C., Liang, J., Huang, M., Bai, B., Bai, K., & Li, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.02763 · Cited by 20 · see citing works
F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version)Preprint
Feldmann, A., Samardzic, N., Krastev, A., Devadas, S., Dreslinski, R., Eldefrawy, K., Genise, N., Peikert, C., & Sánchez, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05371 · Cited by 19 · see citing works
Privacy-preserving collaborative machine learning in biomedical applications
Kim, W., & Seok, J. (2022) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic54071.2022.9722703 · Cited by 18 · see citing works
Wang, J., & Li, J. (2024) · Applied Sciences · doi:10.3390/app14051743 · Cited by 17 · see citing works
Disparate Vulnerability to Membership Inference Attacks
Kulynych, B., Yaghini, M., Cherubin, G., Veale, M., & Troncoso, C. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2022-0023 · Cited by 53 · see citing works
A Hybrid-Domain Framework for Secure Gradient Tree Boosting.Preprint
Fang, W., Chen, C., Tan, J., Yu, C., Lu, Y., Wang, L., Wang, L., Zhou, J., & -, X. A. (2020) · arXiv (Cornell University) · Cited by 17 · see citing works
Verifiable and Provably Secure Machine Unlearning
Eisenhofer, T., Riepel, D., Chandrasekaran, V., Ghosh, E., Ohrimenko, O., & Papernot, N. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00033 · Cited by 16 · see citing works
Non-interactive Privacy Preserving Recurrent Neural Network Prediction with Homomorphic Encryption
Podschwadt, R., & Takabi, D. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00019 · Cited by 15 · see citing works
Federated Learning Application on Telecommunication-Joint Healthcare Recommendation
Song, Y. I., Xie, Y., Zhang, H., Liang, Y., Ye, X., Yang, A., & Ouyang, Y. (2021) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct52962.2021.9657870 · Cited by 15 · see citing works
Self-citationTran, N. Q. H., Prodan, I., Grøtli, E. I., & Lefèvre, L. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107706 · Cited by 14 · see citing works
Qiu, X., Xu, Q., Ge, H., Gao, X., Huang, J., Zhang, H., Wu, X., & Lin, J. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.125020 · Cited by 13 · see citing works
Xia, W., Li, Y., Zhang, L., Wu, Z., & Yuan, X. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3231277 · Cited by 13 · see citing works
Multicenter Privacy-Preserving Cox Analysis Based on Homomorphic Encryption
Lu, Y., Tian, Y., Zhou, T., Zhu, S., & Li, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3071270 · Cited by 12 · see citing works
Quantum computing and neuroscience for 6G/7G networks: Survey
Glisic, S., & Lorenzo, B. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200346 · Cited by 15 · see citing works
Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach
Khan, M. J., Fang, B., Cimino, G., Cirillo, S., Yang, L., & Zhao, D. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00061 · Cited by 12 · see citing works
Bibliometrics of Machine Learning Research Using Homomorphic Encryption
Chen, Z., Hu, G., Zheng, M., Song, X., & Chen, L. (2021) · Mathematics · doi:10.3390/math9212792 · Cited by 12 · see citing works
Efficient Asynchronous Multi-Participant Vertical Federated Learning
Shi, H., Xu, Y., Jiang, Y., Yu, H., & Cui, L. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3201729 · Cited by 12 · see citing works
Approximate Homomorphic Encryption over the Conjugate-Invariant Ring
Kim, D., & Song, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-12146-4_6 · Cited by 12 · see citing works
Self-citation
- 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 › Artificial Intelligence › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
