Management of secret keys for digital signatures is one of the most critical issues in decentralized applications. Since there is no administrator, losing a secret key can result in losing all assets or rights. To address this problem, fuzzy extractors and fuzzy signatures, which generate private keys directly from biometric information, have been considered in addition to conventional biometric authentication. However, these methods using biometric secret keys do not support group signatures. Therefore, it is not applicable to use cases that require consensus building by a specific community (group), such as DAO and DeFi.In this paper, we propose a new scheme for converting existing fuzzy signatures to group signatures using zero-knowledge proofs to address this problem. More precisely, we first define an anonymizable signature that is a generalization of a group signature and then convert a fuzzy signature into an anonymizable signature using an ordinary (classical) zero-knowledge proof. In addition, the signature data size is optimized to a constant size using zk-SNARK. Our implementation experiments show that our schemes achieve practical signature generation and verification times and signature sizes even for a group of up to 100,000 people. This paper’s results can be used to prevent the loss of secret keys and enable flexible DApps use cases.

Various services ranging from finance to public services are digitalized in recent years for higher efficiency and user convenience. With this service digitalization, the need for identifying and authenticating users is increasing. Amongst the user authentication methods, biometric authentication is spreading as it does not require the user to remember a password or to have a specific token. As a more convenient authentication method, research is also being conducted on unconscious authentication using smartphones’ movement history. In this paper, we propose location history-based implicit user authentication acquired through GPS-equipped mobile devices. This method enables hands-free user authentication just by having a mobile device. However, location data are sensitive information that needs to be secured from the risk of location data leakage. By using the template protection technique, location data can be transformed so that the original location data cannot be recovered while enabling authentication. However, it has a trade-off between security and accuracy and remains as a problem to be solved. This paper proposes a new location history matching method based on Modified Weighted Jaccard Coefficient. Then it extends it to template protected location history authentication by presenting a new template protection technique using b-Bit MinHash. Our experimental results show that our proposed location matching method achieves practical accuracy compared with the conventional location history matching method. Furthermore, our template-protected location authentication has comparable accuracy to unprotected matching.

Biometric authentication is one of the promising alternatives to standard password-based authentication offering better usability and security. In this work, we revisit the biometric authentication based on fuzzy signatures introduced by Takahashi et al. (ACNS'15, IJIS'19). These are special types of digital signatures where the secret signing key can be a ''fuzzy'' data such as user's biometrics. Compared to other cryptographically secure biometric authentications as those relying on fuzzy extractors, the fuzzy signature-based scheme provides a more attractive security guarantee. However, despite their potential values, fuzzy signatures have not attracted much attention owing to their theory-oriented presentations in all prior works. For instance, the discussion on the practical feasibility of the assumptions (such as the entropy of user biometrics), which the security of fuzzy signatures hinges on, is completely missing.