Abstract Time-release cryptography is a flourishing research area with a long history and has been extensively studied. In this work, we enrich it by introducing a novel concept: a time-release zero-knowledge proof (TRZKP). A TRZKP is a non-interactive zero-knowledge proof that allows one to publish a proof for a given relation $$R_\mathcal {L}$$ R L , such that anyone can only finish the verification after time $$\textbf{T}$$ T by performing a sequential computation. This work formalizes the concept of TRZKP and presents light constructions for the time-release version of any NIZK obtained from a public-coin protocol via Fiat-Shamir transformation. TRZKPs can be applied to provide time-release authentication, for example, they can be employed to construct verifiable timed signatures (VTS), introduced by Thyagarajan et al. (CCS’20). Through both theoretical and practical analysis, our construction has advantages over existing VTS for Fiat-Shamir signatures. Specifically, when instantiated with Shnorr signature, our VTS signing time remains basically unchanged as the delay time grows, and is preferable for longer delay times; our VTS verification time is significantly small (on the level of milliseconds, while existing works on the level of seconds), and our VTS size is 67 times smaller than the state-of-the-art. It also has the time-verifiability property, which ensures the signature is recoverable after the specified time.

Visible-infrared person re-identification (VI-ReID) aims to match pedestrian images captured by visible and infrared cameras. The main challenge lies in the severe cross-modality and intra-modality differences between visible light (VIS) and infrared (IR) images. Most current CNN-based methods achieve cross-modality retrieval by mapping two images into a high-dimensional subspace and exploiting modality-shared features, making it difficult to mine diverse cross-modality representations and effectively capture global image dependencies. To address these issues, we propose a compact modality alignment network (CMAN) based on the dual stream ViT architecture to explore a novel modality alignment approach for VI-ReID. Specifically, we first deploy a dual deep network based on vision transformer and divide the layers of the self-attention mechanism into heterogeneous and isomorphic modules, allowing us to extract modality-specific features and shared features of each image at different stages. Then, in the heterogeneous module, we use multiple class tokens in each modality to represent multiple embedding spaces and apply a Dynamic Controller (DC) in these spaces to push each class token away from each other by adaptively adjusting the weight of each class token, which makes each embedding space diverse and compact, thereby improving the discrimination of modality-specific features in each modality. Finally, in the isomorphic part, we use the Token Permutation (TP) module to permute and concatenate class tokens in different modalities. Not only helps align shared features of modalities, it also allows the class token in the current modality to perceive the local details of another modality. Extensive experiments on the public SYSU-MM01, RegDB, and LLCM datasets demonstrate the superiority of the proposed CMAN over state-of-the-art methods.

Abstract Zero knowledge succinct non-interactive arguments of knowledge protocol (zk-SNARK) is an application oriented variant of zero knowledge proof, which enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities and high efficiency, it has been widely deployed in various blockchain based applications to provide privacy and scalability. While these applications place high demands on small proof size, fast verification and decentralization, currently available zk-SNARK with the shortest proof size and the fastest verification speed is in the common reference string (CRS) model, that is they require the trusted setup. After the pioneering results proposed by Bellare et al. in ASIACRYPT 2016, there have been lots of efforts to construct zk-SNARKs that satisfy subversion zero knowledge (S-ZK) and standard soundness from the zk-SNARK in the CRS model. These constructions could be regarded secure in the bare public key (BPK) model because that the equivalence between S-ZK in the CRS model, and uniform non-black-box zero knowledge in the BPK model has been proved by Abdolmaleki et al. in PKC 2020. Thus, compared to the CRS model, the BPK model better characterizes decentralized blockchain based application such as cryptocurrencies and anonymous credentials. In this study, by leveraging the power of random oracle (RO) model, we proposed the first publicly verifiable non-uniform ZK zk-SNARK scheme in the BPK model maintaining comparable efficiency with its conventional counterpart, which can also be compatible with the well-known transformation proposed by Bitansky et al. in TCC 2013 to obtain an efficient designated-verifier zk-SNARK. We achieve this goal by only adding a constant number of elements into the CRS, and using an unconventional but natural method to transform Groth’s zk-SNARK in EUROCRYPT 2016. In addition, we propose a new speed-up technique that provides a trade-off. Specifically, if a logarithmic number of elements are added into the CRS, according to different circuits, the CRS verification time in our construction could be approximately 9–23% shorter than that in the conventional counterpart.

Confidentiality is an indispensable requirement in financial applications of blockchain technology, and supporting it along with high performance and friendly programmability is technically challenging. In this paper, we present a system design called CONFIDE to support on-chain confidentiality by leveraging Trust Execution Environment (TEE). CONFIDE's secure data transmission protocol and data encryption protocol, together with a highly efficient virtual machine run in TEE, guarantee the confidentiality in the life cycle of a transaction from end to end. CONFIDE proposes a secure data model along with an application-driven secure protocol to guarantee data confidentiality and integrity. Its smart contract language extension offers users the flexibility to define complex confidentiality models. CONFIDE is implemented as a plugin module to Antfin Blockchain's proprietary platform, and can be plugged into other blockchain platforms as well with its universal interface design. Nowadays, CONFIDE is supporting millions of commercial transactions daily on consortium blockchain running financial applications including supply chain finance, ABS, commodity provenance, and cold-chain logistics.