| Title | Privacy-Preserving Biometric Matching Using Homomorphic Encryption |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Pradel, Gaëtan, Mitchell, Chris |
| Conference Name | 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
| Date Published | oct |
| Keywords | biometric encryption, biometrics (access control), Biometrics Homomorphic, data privacy, Encryption, Libraries, Metrics, multiparty computation, performance evaluation, privacy, Privacy-preserving, Protocols, pubcrawl, resilience, Resiliency, Scalability, Servers |
| Abstract | Biometric matching involves storing and processing sensitive user information. Maintaining the privacy of this data is thus a major challenge, and homomorphic encryption offers a possible solution. We propose a privacy-preserving biometrics-based authentication protocol based on fully homomorphic en-cryption, where the biometric sample for a user is gathered by a local device but matched against a biometric template by a remote server operating solely on encrypted data. The design ensures that 1) the user's sensitive biometric data remains private, and 2) the user and client device are securely authenticated to the server. A proof-of-concept implementation building on the TFHE library is also presented, which includes the underlying basic operations needed to execute the biometric matching. Performance results from the implementation show how complex it is to make FHE practical in this context, but it appears that, with implementation optimisations and improvements, the protocol could be used for real-world applications. |
| DOI | 10.1109/TrustCom53373.2021.00079 |
| Citation Key | pradel_privacy-preserving_2021 |
Privacy-Preserving Biometric Matching Using Homomorphic Encryption