| Title | A Study on a DDH-Based Keyed Homomorphic Encryption Suitable to Machine Learning in the Cloud |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Tsuruta, Takuya, Araki, Shunsuke, Miyazaki, Takeru, Uehara, Satoshi, Kakizaki, Ken'ichi |
| Conference Name | 2022 IEEE International Conference on Consumer Electronics – Taiwan |
| Keywords | Consumer electronics, homomorphic encryption, Human Behavior, human factors, keyed homomorphic public key encryption, machine learning, Metrics, pubcrawl, Public key, resilience, Resiliency, Scalability |
| Abstract | Homomorphic encryption is suitable for a machine learning in the cloud such as a privacy-preserving machine learning. However, ordinary homomorphic public key encryption has a problem that public key holders can generate ciphertexts and anyone can execute homomorphic operations. In this paper, we will propose a solution based on the Keyed Homomorphic-Public Key Encryption proposed by Emura et al. |
| DOI | 10.1109/ICCE-Taiwan55306.2022.9869098 |
| Citation Key | tsuruta_study_2022 |
A Study on a DDH-Based Keyed Homomorphic Encryption Suitable to Machine Learning in the Cloud