In a competitive market, online auction systems enable optimal trading of digital products and services. Bidders can participate in existing blockchain-based auctions while protecting the confidentiality of their bids in a decentralized, transparent, secure, and auditable manner. However, in a competitive market, parties would prefer not to disclose their interests to competitors, and to remain anonymous during auctions. In this paper, we firstly analyze the specific requirements for blockchain-based anonymous fair auctions. We present a formal model tailored to study auction systems that facilitate anonymity, as well as a generic protocol for achieving bid confidentiality and bidder anonymity using existing cryptographic primitives such as designated verifier ring signature. We demonstrate that it is secure using the security model we presented. Towards the end, we demonstrate through extensive simulation results on Ethereum blockchain that the proposed protocol is practical and has minimal associated overhead. Furthermore, we discuss the complexity and vulnerabilities that a blockchain environment might introduce during implementation.

We point out that the quantum digital signature scheme proposed in ICACT 2005 has three problems. According to the original description of the scheme, we find: (1) the quantum one-way function is not specified clearly; (2) the signer Alice does not use her private key in the signing process; (3) both the signing and the verification can not work well.

In recent years, the rapid growth of Internet of Things (IoT) technologies has created numerous opportunities across fields such as smart cities, transportation, energy, and healthcare

The signature constrained two-stage framework for hyperspectral target detection (IEEE TGRS, 5514917, 2025) needs to predict whether the label of each encoded feature vector belongs to the target category. The process computes the inner product between the feature vector and each signature feature vector. It claims that the product reflects the similarity between the two vectors, with higher values indicating greater similarity. We find this claim is generally not valid without further assumptions. We stress that the general inner product cannot be used to indicate the Euclidean distance of two vectors. We show that the distance-compatible similarity score can reflect the true similarity between any two vectors with the same dimension.