Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption

Cheon, Jung Hee; Kim, Duhyeong; Kim, Yongdai; Song, Yongsoo

doi:10.1109/access.2018.2866697

Cited by 60 publications

(33 citation statements)

References 15 publications

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“…This approximate concept of CKKS makes sense due to most of the data in real world applications being noisiness. In this setting, the CKKS scheme works perfectly in practice [5]. Using the CKKS scheme, many works addressed real world problems.…”

Section: Homomorphic Encryptionmentioning

confidence: 97%

“…Due to this attractive functionality, HE has received much attention recently for preserving sensitive information (e.g., financial data). Although there also exist other cryptographic tools such as secure multiparty computation (MPC), HE has relative advantages compared to MPC since it supports no-interactive operation and fits perfectly in matrix and vector operations [5].…”

Section: Introductionmentioning

confidence: 99%

“…Several works are proposed to reduce computational overhead. For example, Cheon et al [5] proposed an ensemble method for logistic regreesion based on HE, which resulted in substantial improvement on the performance of logistic regression. Zhang et al [6] proposed GELU-Net, which was a novel privacy-preserving architecture where clients can collaboratively train a deep neural network model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Privacy-Preserved Approximate Classification Based on Homomorphic Encryption

Chen

Fan

2019

MCA

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Privacy is a crucial issue for outsourcing computation, which means that clients utilize cloud infrastructure to perform online prediction without disclosing sensitive information. Homomorphic encryption (HE) is one of the promising cryptographic tools resolving privacy issue in this scenario. However, a bottleneck in application of HE is relatively high computational overhead. In this paper, we study the privacy-preserving classification problem. To this end, we propose a novel privacy-preserved approximate classification algorithm. It exploits a set of decision trees to reduce computational complexity during homomorphic evaluation computation formula, the time complexity of evaluating a polynomial is degraded from O n to O log n . As a result, for an MNIST dataset, the Micro- f 1 score of the proposed algorithm is 0 . 882 , compared with 0 . 912 of the standard method. For the Credit dataset, the algorithm achieves 0 . 601 compared with 0 . 613 of the method. These results show that our algorithm is feasible and practical in real world problems.

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Section: Homomorphic Encryptionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Privacy-Preserved Approximate Classification Based on Homomorphic Encryption

Chen

Fan

2019

MCA

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“…For the multiple linear regression (d > 1), we set the initial values to (N, d, l, p) � (10, 2, 16, 1) for the experiment. e dataset consists of feature vectors x 1 � [2,4,5,6,8,10,13,16,17,19], x 2 � [3,5,6,7,8,11,14,15,18,20], and a target variable y � [5,9,12,14,15,18,24,26,30, 32] with 10 data created artificially, and it takes 1047 seconds with 0.01 running rate. e iteration proceeded 50 steps to converge θ � (− 0.952, 1.094, 3.331) with threshold value, ε � 0.1.…”

Section: Performance Evaluation Of Fhe Linear Regressionmentioning

confidence: 99%

“…proposed a least square polynomial that broadens bounded domain of Taylor series expansion to (− 8, 8) [19,20]. e underlying principle is to derive a function g(x) that minimizes mean squared error (MSE) such that 1/|I|…”

Section: Least Square Approximation Kim and Cheon Et Almentioning

confidence: 99%

A Bitwise Design and Implementation for Privacy-Preserving Data Mining: From Atomic Operations to Advanced Algorithms

Song

Yoo

Hong

et al. 2019

Security and Communication Networks

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Homomorphic encryption (HE) is considered as one of the most powerful solutions to securely protect clients' data from malicious users and even severs in the cloud computing. However, though it is known that HE can protect the data in theory, it has not been well utilized because many operations of HE are too slow, especially multiplication. In addition, existing data mining research studies using encrypted data focus on implementing only speci c algorithms without addressing the fundamental problem of HE. In this paper, we propose a fundamental design and implementation of data mining algorithm through logical gates. In order to do this, we design various logic of atomic operations in encrypted domain and nally apply these logic to wellknown data mining algorithms. We also analyze the execution time of atomic and advanced algorithms.

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Approximate Homomorphic Encryption over the Conjugate-Invariant Ring

Kim

Song

2019

Lecture Notes in Computer Science

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Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption

Cited by 60 publications

References 15 publications

Privacy-Preserved Approximate Classification Based on Homomorphic Encryption

Privacy-Preserved Approximate Classification Based on Homomorphic Encryption

A Bitwise Design and Implementation for Privacy-Preserving Data Mining: From Atomic Operations to Advanced Algorithms

Approximate Homomorphic Encryption over the Conjugate-Invariant Ring

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