Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.
Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.
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DOI：
10.2196/medinform.8805
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发表时间：
2018-04-17
影响因子：
3.2
通讯作者：
Jiang X
中科院分区：
文献类型：
--
作者：
Kim M;Song Y;Wang S;Xia Y;Jiang X
多年来，在不访问原始数据的情况下学习模型一直是安全和机器学习研究人员的一个有趣想法。在理想的情况下，我们希望加密敏感数据，将其存储在商业云上，并在不解密数据的情况下运行某些分析，以保护隐私。同态加密技术是一种很有前途的安全数据外包技术，但它是一个非常具有挑战性的任务，以支持现实世界的机器学习任务。现有的框架只能处理简单的情况下，低次多项式，如线性均值分类器和线性判别分析。本研究的目的是为主流的学习模式（如逻辑回归）提供一个实际的支持。我们采用了一种新的同态加密方案优化真实的数字计算。我们设计了（1）最小二乘近似的逻辑函数的准确性和效率（即，降低计算成本）和（2）新的包装和并行化技术。使用真实世界的数据集，我们评估了我们的模型的性能，并证明了其在速度和内存消耗的可行性。例如，从同态加密的Edinburgh数据集获得训练模型大约需要116分钟。此外，它对测试数据集给出了相当准确的预测。我们提出了第一个同态加密的逻辑回归外包模型的基础上的关键观察，分类模型的精度损失是足够小，使决策计划保持不变。
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. The goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression). 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. 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. 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.