Logistic regression model training based on the approximate homomorphic encryption

By Andrey Kim, Yongsoo Song, Miran Kim, Keewoo Lee, Jung Hee Cheon. 2018

242 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

HEAX: An Architecture for Computing on Encrypted Data (2019, methodology): “ct computation on encrypted data. A recently proposed FHE scheme called CKKS [17] performs approximate computation of real numbers and supports efficient truncation of encrypted values. Several works [40, 43] have shown the benefits of choosing the CKKS scheme over other schemes when an approximate ”
Towards Deep Neural Network Training on Encrypted Data (2019, background): “Some prior work described how to preform training and inference for other types of models on encrypted data, speciﬁcally linear-regression models [24] and even logistic-regression models [29, 15, 19, 18, 10].”
Labeled PSI from Fully Homomorphic Encryption with Malicious Security (2018, methodology): “evaluating the AES circuit [25], computing edit distance on DNA sequences [16], and training logistic regression models [34].”
HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, background): “Several works [51, 47] have shown the benefits of choosing the CKKS scheme over other schemes when an approximate computation is required, e.”
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019, methodology): “One is based on Homomorphic Encryption (HE) (Gentry & Boneh, 2009) which allows for computation to be performed over encrypted data, and has been used to enable privacy-preserving machine learning solutions (GiladBachrach et al., 2016; Hesamifard et al., 2017; Graepel et al., 2012; Yuan & Yu, 2014; ”
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning (2020, background): “The first one is Homomorphic Encryption (HE) [15], which enables computations on encrypted data, and has been applied to privacy-preserving machine learning [16, 20, 17, 41, 24, 22, 38, 19].”
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “Hence, HE schemes with packing techniques [6, 7, 16, 22] have good amortized complexity per plaintext value, and they have been applied to privacy-preserving big data analysis [11, 37, 39].”