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.

In this study, we define two novel classes of lattices: the equiangular lattice$\mathcal{E}_{n}$and semi-equiangular lattice$\tilde{\mathcal{E}}_{n}$. We propose novel algorithms for solving the closest vector problem in$\mathcal{E}_{n}$and$\mathcal{E}_{n}$in$O(n \log n)$time. Furthermore, we develop improved algorithms with$O(n)$time for slightly restricted classes.$\mathcal{E}_{n}$includes the well-studied root lattice$A_{n}$and its dual,$A_{n}^{*}. \tilde{\mathcal{E}}_{n}$includes another important root lattice$D_{n}$(or the checkerboard lattice) and its dual,$D_{n}^{*}. \tilde{\mathcal{E}}_{n}$also includes the Coxeter-Barnes lattice$A_{n}^{r}$, which includes many important lattices lying between$A_{n}$and$A_{n}^{*}$, including$A_{n}, E_{7}, E_{8}$and their duals$A_{n}^{*}, E_{7}^{*}, E_{8}^{*}$.

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.

With the growth of image on the web, research on hashing which enables high-speed image retrieval has been actively studied. In recent years, various hashing methods based on deep neural networks have been proposed and achieved higher precision than the other hashing methods. In these methods, multiple losses for hash codes and the parameters of neural networks are defined. They generate hash codes that minimize the weighted sum of the losses. Therefore, an expert has to tune the weights for the losses heuristically, and the probabilistic optimality of the loss function cannot be explained. In order to generate explainable hash codes without weight tuning, we theoretically derive a single loss function with no hyperparameters for the hash code from the probability distribution of the images. By generating hash codes that minimize this loss function, highly accurate image retrieval with probabilistic optimality is performed. We evaluate the performance of hashing using MNIST, CIFAR-10, SVHN and show that the proposed method outperforms the state-of-the-art hashing methods.

A cancelable biometric scheme called correlation-invariant random filtering (CIRF) is known as a promising template protection scheme. This scheme transforms a biometric feature represented as an image via the 2D number theoretic transform (NTT) and random filtering. CIRF has perfect secrecy in that the transformed feature leaks no information about the original feature. However, CIRF cannot be applied to large-scale biometric identification, since the 2D inverse NTT in the matching phase requires high computational time. Furthermore, existing biometric indexing schemes cannot be used in conjunction with template protection schemes to speed up biometric identification, since a biometric index leaks some information about the original feature. In this paper, we propose a novel indexing scheme called “cancelable indexing” to speed up CIRF without losing its security properties. The proposed scheme is based on fast computation of CIRF via low-rank approximation of biometric images and via a minimum spanning tree representation of low-rank matrices in the Fourier domain. We prove that the transformed index leaks no information about the original index and the original biometric feature (i.e., perfect secrecy), and thoroughly discuss the security of the proposed scheme. We also demonstrate that it significantly reduces the one-to-many matching time using a finger-vein dataset that includes six fingers from 505 subjects.

Color correction is an essential image processing operation that transforms a camera-dependent RGB color space to a standard color space, e.g., the XYZ or the sRGB color space. The color correction is typically performed by multiplying the camera RGB values by a color correction matrix, which often amplifies image noise. In this paper, we propose an effective color correction pipeline for a noisy image. The proposed pipeline consists of two parts; the color correction and denoising. In the color correction part, we utilize spatially varying color correction (SVCC) that adaptively calculates the color correction matrices for each local image block considering the noise effect. Although the SVCC can effectively suppress the noise amplification, the noise is still included in the color corrected image, where the noise levels spatially vary for each local block. In the denoising part, we propose an effective denoising framework for the color corrected image with spatially varying noise levels. Experimental results demonstrate that the proposed color correction pipeline outperforms existing algorithms for various noise levels.

Likelihood-ratio based biometric score fusion (LR fusion) has attracted attention since it maximizes accuracy if a log-likelihood ratio (LLR) is accurately estimated. It can also allow a user to select a subset of modalities at the authentication phase by setting LLRs corresponding to missing query samples to 0 (we refer to LR fusion with/without this mode as selective/non-selective LR fusion). However, a recent study proposed a modality selection attack, in which an impostor inputs only query samples whose LLRs are larger than 0 (i.e. takes an optimal strategy), against selective LR fusion, and showed that it degrades overall accuracy even if a genuine user also takes this optimal strategy. In this paper, we investigate the impact of the modality selection attack in more details. Specifically, we study whether the overall accuracy is improved by eliminating “goat” templates, whose LLRs tend to be less than or equal to 0 for genuine users. We investigate, both theoretically and experimentally, whether this restriction of modalities (i.e. elimination of goat templates) increases the KL (Kullback-Leibler) divergence between a genuine score distribution and an impostor's one, which can be compared with password entropy. We first show a negative result that the restriction of modalities hardly increases the KL divergence in selective LR fusion. We then show that it can increase the KL divergence in non-selective LR fusion.

It is known that different users have different degrees of accuracy in biometric authentication, and claimants and enrollees who cause false accepts against many others are referred to as wolves and lambs, respectively. The aim of this paper is to develop a fusion algorithm, which has security against both of the animals while minimizing the number of query samples a genuine claimant has to input. To achieve our aim, we first introduce a taxonomy of wolves and lambs, and propose a minimum log-likelihood ratio-based sequential fusion scheme (MLR scheme). We prove that this scheme keeps wolf attack probability and lamb accept probability, the maximum of the claimant-specific false accept probability (FAP), and the enrollee-specific FAP, less than a desired value if log-likelihood ratios are perfectly estimated, except in the case of adaptive spoofing wolves. We also prove that this scheme is optimal with regard to false reject probability (FRP), and asymptotically optimal with respect to the average number of inputs (ANIs) under some conditions. We further propose an input order decision scheme based on the Kullback-Leibler (KL) divergence, which maximizes the expectation of a genuine log-likelihood ratio, to further reduce ANI of the MLR scheme in the case where the KL divergence differs from one modality to another. The results of the experimental evaluation using a virtual multimodal (one face and eight fingerprints) data set showed the effectiveness of our schemes.

Biometric identification provides a very convenient way to authenticate a user because it does not require the user to claim an identity. However, both the identification error rates and the response time increase almost in proportion to the number of enrollees. A technique which decreases both of them using only scores has the advantage that it can be applied to any kind of biometric system that out- puts scores. In this paper, we propose such a technique by combining score level fusion and distance-based indexing. In order to reduce the retrieval error rate in multibiometric identification, our technique takes a strategy to select the template of the enrollee whose posterior probability of be- ing identical to the claimant is the highest as a next to be matched. The experimental evaluation using the Biosecure DS2 dataset and the CASIA-FingerprintV5 showed that our technique significantly reduced the identification error rates while keeping down or even reducing the number of score calculations, compared to the unimodal biometrics.

The Correlation Invariant Random Filtering (or CIRF) is an algorithm for cancelable biometrics, and known to have provable security. However, the security proof requires a strong assumption with regard to biometric features, which is rarely satisfied in practice. In this paper we examine the security of the CIRF when the assumption is not satisfied, and show that there are problems in secrecy of the feature and diversity of cancelable templates. To address these problems, we interpret the CIRF from an algebraic point of view, and generalize it based on a quotient polynomial ring. Then we prove several theorems which derive a new transformation algorithm for cancelable biometrics. The proposed algorithm has provable security without any condition of biometric features.

We aim at reducing the number of distance computations as much as possible in the inexact indexing schemes which sort the objects according to some promise values. To achieve this aim, we propose a new probability-based indexing scheme which can be applied to any inexact indexing scheme that uses the promise values. Our scheme (1) uses the promise values obtained from any inexact scheme to compute the new probability-based promise values. In order to estimate the new promise values, we (2) use the object-specific parameters in logistic regression and learn the parameters using MAP (Maximum a Posteriori) estimation. We also propose a technique which (3) speeds up learning the parameters using the promise values. We applied our scheme to the standard pivot-based scheme and the permutation-based scheme, and evaluated them using various kinds of datasets from the Metric Space Library. The results showed that our scheme improved the conventional schemes, in all cases.

We propose the minimum distance entropy (MDE) as a metric of biométrie information content. The MDE is the probability that two biométrie samples correspond exactly expressed in information content and can be calculated through the experiment for interpersonal matching using a set of biométrie samples. This metric makes it possible for certain biometrics not only to be compared with other biometrics but also to be partially compared with personal authentication using passwords, PIN, or other methods in regard to the identification performance or the security. In this paper, we discuss the metric in terms of information theory and show how to evaluate it. Then, as an example, we apply it to a fingerprint system and evaluate fingerprint information content through simulations.