All-but-one Vector Commitments (AVCs) randomly opens all but one of the committed vector values. Typically AVCs are instantiated using Goldwasser-Goldreich-Micali (GGM) trees. Generating these trees comprises a significant computational cost for AVCs due to a large number of hash function calls. Correlated GGM (cGGM) trees have been proposed to halve the number of hash calls and Batched AVCs (BAVCs) using a single GGM tree were integrated in the FAEST signature scheme, which improves efficiency and reduces the signature sizes. This paper proposes BACON, a BAVC with aborts that leverages a single cGGM tree. BACON executes multiple instances of AVC in a single batch and enables an abort mechanism to probabilistically reduce the commitment size. We prove that BACON is secure under the ideal cipher model and the random oracle model. We also discuss the possible application of the proposed BACON and show the theoretical efficiency compared to state-of-the-art.

Abstract The World Wide Web Consortium (W3C) has established standards for decentralized identities (DIDs) and verifiable credentials (VCs). A DID serves as a unique identifier for an entity, while a VC validates specific attributes associated with the DID holder. To prove ownership of credentials, users generate verifiable presentations (VPs). To enhance privacy, the W3C standards advocate for randomizable signatures in VC creation and zero-knowledge proofs for VP generation. However, these standards face a significant limitation: they cannot effectively verify cross-domain credentials while maintaining anonymity. In this paper, we present Anonymous Verifiable Presentations with Extended Usability (AVPEU), a novel framework that addresses this limitation through the introduction of a notary system. At the technical core of AVPEU lies our proposed randomizable message-hiding signature scheme. We provide both a generic construction of AVPEU and specific implementations based on Boneh–Boyen–Shacham, Camenisch–Lysyanskaya, and Pointcheval–Sanders signature. Our experimental results demonstrate the feasibility of these schemes.

Concerns about how third parties manage personal information have led to the development of decentralized identities (DIDs) and verifiable credentials (VCs). The World Wide Web Consortium (W3C) working group has been developing standards for DIDs and VCs. In the W3C standards, a DID identifies an entity (a DID holder) and a VC confirms that this DID holder has some associated attributes. A DID holder can obtain many VCs and confirm any number of these VCs to others (verifiers) in verifiable presentations (VPs). In order to keep a holder’s identity and attributes private, it is necessary to achieve anonymous VPs that allows this information to be kept confidential. The W3C working group recommends using randomizable signatures to create VCs with zero-knowledge proofs for this purpose. However, the anonymous VPs provided by the this method are limited that in the real world, credentials in cross domains cannot be universally verified. To overcome this limitation, in this paper, we propose a new scheme, called Verifiable Credentials with anonymous DIDs (VCaDID), which aims to achieve anonymous VPs in cross-domain settings. The main technique in our VCaDID scheme is a ring signature with multiple attributes by hiding a holder’s public key among a ring of holders. In our scheme, we set private keys associated with the holder’s DID and attributes, which allow the holder to anonymously present these credentials in a verifiable way. We also prove that the proposed VCaDID scheme satisfies correctness, anonymity and unforgeability under security assumptions of discrete log and random oracle model. Finally, we implement our scheme to demonstrate its feasibility.

Group signatures and their variants have been widely used in privacy-sensitive scenarios such as anonymous authentication and attestation. In this paper, we present a new post-quantum group signature scheme from symmetric primitives. Using only symmetric primitives makes the scheme less prone to unknown attacks than basing the design on newly proposed hard problems whose security is less well-understood. However, symmetric primitives do not have rich algebraic properties, and this makes it extremely challenging to design a group signature scheme on top of them. It is even more challenging if we want a group signature scheme suitable for real-world applications, one that can support large groups and require few trust assumptions. Our scheme is based on MPC-in-the-head non-interactive zero-knowledge proofs, and we specifically design a novel hash-based group credential scheme, which is rooted in the SPHINCS+ signature scheme but with various modifications to make it MPC (multi-party computation) friendly. The security of the scheme has been proved under the fully dynamic group signature model. We provide an implementation of the scheme and demonstrate the feasibility of handling a group size as large as 2 60 . This is the first group signature scheme from symmetric primitives that supports such a large group size and meets all the security requirements.

Mapping surface water bodies from fine spatial resolution optical remote sensing imagery is essential for the understanding of the global hydrologic cycle. Although satellite data are useful for mapping, the limited spectral information captured by some satellite systems can be suboptimal for the task. For example, the very high-resolution images of Google Earth (GE) only contain RGB bands, which often means many water bodies and land objects are confused. Sentinel-2 (S2) imagery has a spectral resolution more suitable for mapping water bodies, but its medium spatial resolution limits the ability for detailed mapping of water-land boundaries. This letter proposes a deep feature and domain knowledge fusion network (DFDKFNet) for mapping surface water bodies by fusing GE and S2 images while incorporating domain knowledge. DFDKFNet uses the remote sensing indices of normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) derived from the S2 image as the representative domain knowledge to better extract water bodies from terrestrial features. A similar pixel-based approach is used to downscale the NDWI and NDVI maps to match the spatial resolution between the GE and S2 images. The DFDKFNet uses the GE and downscaled NDWI and NDVI images to extract the deep semantic features of water bodies, which are fused with the domain knowledge extracted from the NDWI and NDVI images. DFDKFNet was compared with several state-of-the-art algorithms, and the results show that DFDKFNet can enhance water body mapping accuracy.

Spatiotemporal satellite image fusion (STIF) has been widely applied in land surface monitoring to generate high spatial and high temporal reflectance images from satellite sensors. This paper proposed a new unmixing-based spatiotemporal fusion method that is composed of a self-trained random forest machine learning regression (R), low resolution (LR) endmember estimation (E), high resolution (HR) surface reflectance image reconstruction (R), and residual compensation (C), that is, RERC. RERC uses a self-trained random forest to train and predict the relationship between spectra and the corresponding class fractions. This process is flexible without any ancillary training dataset, and does not possess the limitations of linear spectral unmixing, which requires the number of endmembers to be no more than the number of spectral bands. The running time of the random forest regression is about ~1% of the running time of the linear mixture model. In addition, RERC adopts a spectral reflectance residual compensation approach to refine the fused image to make full use of the information from the LR image. RERC was assessed in the fusion of a prediction time MODIS with a Landsat image using two benchmark datasets, and was assessed in fusing images with different numbers of spectral bands by fusing a known time Landsat image (seven bands used) with a known time very-high-resolution PlanetScope image (four spectral bands). RERC was assessed in the fusion of MODIS-Landsat imagery in large areas at the national scale for the Republic of Ireland and France. The code is available at https://www.researchgate.net/proiile/Xiao_Li52.