# Fast and Secure Linear Regression and Biometric Authentication with Security Update

@article{Aono2015FastAS, title={Fast and Secure Linear Regression and Biometric Authentication with Security Update}, author={Yoshinori Aono and Takuya Hayashi and Le Trieu Phong and Lihua Wang}, journal={IACR Cryptol. ePrint Arch.}, year={2015}, volume={2015}, pages={692} }

We explicitly present a homomorphic encryption scheme with a flexible encoding of plaintexts. We prove its security under the LWE assumption, and innovatively show how the scheme can be used to handle computations over both binary strings and real numbers. In addition, using the scheme and its features, we build fast and secure systems of • linear regression using gradient descent, namely finding a reasonable linear relation between data items which remain encrypted. Compared to the best…

## 25 Citations

### Secure Face Matching Using Fully Homomorphic Encryption

- Computer ScienceBTAS
- 2018

The practicality of using a fully homomorphic encryption based framework to secure a database of face templates, designed to preserve the privacy of users and prevent information leakage from the templates, while maintaining their utility through template matching directly in the encrypted domain is explored.

### Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

- Computer Science, MathematicsIEICE Trans. Inf. Syst.
- 2016

This paper proposes a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption, and shows that only additively homomorphicryption is needed to build this system.

### Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption

- Computer Science, MathematicsIACR Cryptol. ePrint Arch.
- 2017

This work proposes a novel system that can train a ridge linear regression model using only LHE (i.e., without using Yao’s protocol) and greatly improves the overall performance as Yao's protocol was the main bottleneck in the previous solution.

### Highly Scalable Beaver Triple Generator from Additive-only Homomorphic Encryption

- Computer Science, MathematicsAINA
- 2022

This paper studies secure yet efficient Beaver triple generators leveraging privacy-preserving scalar product protocols which in turn can be constructed from additive-only homomorphic encryptions(AHEs), and proposes an alternative construction of AHE from polynomial ring learning with error (RLWE).

### Efficient Key-Rotatable and Security-Updatable Homomorphic Encryption

- Computer Science, MathematicsSCC@AsiaCCS
- 2017

This paper formalises syntax and security notions for KR-SU-HE schemes and builds a concrete scheme based on the Learning With Errors assumption, which is a class of public-key homomorphic encryption in which the keys and the security of any ciphertext can be rotated and updated while still keeping the underlying plaintext intact and unrevealed.

### Privacy-Preserving Ridge Regression over Distributed Data from LHE ∗

- Computer Science, Mathematics
- 2017

This work proposes a novel system that can train a ridge linear regression model using only linearly-homomorphic encryption (i.e., without using Yao’s protocol), which greatly improves the overall performance as Yao's protocol was the main bottleneck in the previous solution.

### Scalable and Secure Logistic Regression via Homomorphic Encryption

- Computer Science, MathematicsIACR Cryptol. ePrint Arch.
- 2016

Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, it is shown that only additively homomorphic encryption is needed to build this system, which is secure and scalable with the dataset size.

### Privacy-Preserving Ridge Regression on Distributed Data

- Computer Science, MathematicsIACR Cryptol. ePrint Arch.
- 2017

A new system that can train a linear regression model with 2-norm regularization (i.e. ridge regression) on a dataset obtained by merging a finite number of private datasets and is based on a simple homomorphic encryption scheme.

### Privacy preserving extreme learning machine using additively homomorphic encryption

- Computer Science2017 IEEE Symposium Series on Computational Intelligence (SSCI)
- 2017

This work proposes a privacy preserving machine learning algorithm for Extreme Learning Machine (PP-ELM), which can learn from data encrypted with an additively homomorphic encryption, and allows us to learn multiple sources of personal data in a secure way.

### Implementing ML Algorithms with

- Computer Science, Mathematics
- 2017

This work implements and analyzes the performance of linear regression and K-means clustering using the homomorphic encryption library SEAL and provides an extension of the SEAL library to matrix operations.

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