Privacy-preservation for Stochastic Gradient Descent Application to Secure Logistic Regression

By Shuang Wu, Tadanori Teruya, Junpei Kawamoto. 2013

24 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Miran Kim
2. Yongsoo Song
3. Andrey Kim
4. Jung Hee Cheon
5. Hao Chen
6. Keewoo Lee
7. Bo Liu
8. Sina Shaham

Citations in the corpus

• ABY3: A Mixed Protocol Framework for Machine Learning (2018, unknown): “[62].”
• Logistic regression model training based on the approximate homomorphic encryption (2018, methodology): “[9] used Paillier cryptosystem [10] and approximated the logistic function using polynomials, but it required an exponentially growing computational cost in the degree of the approximation polynomial.”
• When Machine Learning Meets Privacy (2020, methodology): “ut having access to both the data and the model. SMC has been used for a variety of traditional ML models, including decision trees [7], linear regression [30, 66, 107, 135, 136], logistic regression [143, 170], Naive Bayes classifiers [157], and �-means clustering [16,62]. In general, SMC technique”