Logistic regression model training based on the approximate homomorphic encryption.
Logistic regression model training based on the approximate homomorphic encryption.
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DOI：
10.1186/s12920-018-0401-7
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发表时间：
2018-10-11
影响因子：
2.7
通讯作者：
Cheon JH
中科院分区：
文献类型：
--
作者：
Kim A;Song Y;Kim M;Lee K;Cheon JH
自大数据成为数据分析的重要工具以来，安全问题一直受到关注。例如，许多机器学习算法旨在使用包含有关个人的敏感信息的训练数据来生成预测模型。密码学社区正在考虑将安全计算作为隐私保护的解决方案。特别是，实际需求引发了对密码原语效率的研究。提出了一种无信息泄漏的Logistic回归模型训练方法。我们将Cheon等人的同态加密方案（ASIACRYPT 2017）应用于真实的数字上的有效算法，并设计了一种新的编码方法来减少加密数据库的存储。此外，我们适应Nesterov的加速梯度方法，以减少迭代次数以及计算成本，同时保持输出分类器的质量。我们的方法显示了一个国家的最先进的性能同态加密系统在现实世界中的应用。基于这项工作的提交被选为2017年iDASH隐私和安全竞赛的Track 3最佳解决方案。例如，给定由1579个样本组成的数据集，每个样本具有18个特征和一个二进制结果变量，需要大约6分钟才能获得逻辑回归模型。我们提出了一个实用的解决方案，外包分析工具，如逻辑回归分析，同时保持数据的机密性。本文的在线版本（10.1186/s12920-018-0401-7）包含补充材料，可供授权用户使用。
Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. The online version of this article (10.1186/s12920-018-0401-7) contains supplementary material, which is available to authorized users.
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DOI：
10.1136/amiajnl-2011-000735
发表时间：
2013-05-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
El Emam K;Samet S;Arbuckle L;Tamblyn R;Earle C;Kantarcioglu M
通讯作者：
Kantarcioglu M
影响因子：
1.7
作者：
DIETZ, E
通讯作者：
DIETZ, E
DOI：
10.1109/jbhi.2016.2636665
发表时间：
2017-01-01
影响因子：
7.7
作者：
Ravi, Daniele;Wong, Charence;Yang, Guang-Zhong
通讯作者：
Yang, Guang-Zhong
影响因子：
13.2
作者：
LOWRIE, EG;LEW, NL
通讯作者：
LEW, NL
影响因子：
1.3
作者：
SAMUEL, AL
通讯作者：
SAMUEL, AL