Biblio
Filters: Author is Wang, Lihua [Clear All Filters]
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2017. Efficient Key-Rotatable and Security-Updatable Homomorphic Encryption. Proceedings of the Fifth ACM International Workshop on Security in Cloud Computing. :35–42.
In this paper we presents the notion of key-rotatable and security-updatable homomorphic encryption (KR-SU-HE) scheme, 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. We formalise syntax and security notions for KR-SU-HE schemes and then build a concrete scheme based on the Learning With Errors assumption. We then perform testing implementation to show that our proposed scheme is efficiently practical.
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2016. Scalable and Secure Logistic Regression via Homomorphic Encryption. Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy. :142–144.
Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size.