Financial institutions using blockchain smart contracts need to adhere to real-world regulations. Data on blockchain is easily accessible, so privacy protection is crucial. Our goal is to introduce an efficient protocol that satisfies both user privacy protection and hierarchical regulatory requirements, without the need for zero-knowledge proofs. To achieve this, we have developed two innovative design strategies. Firstly, we envision financial institutions serving as transaction mixers for their users. This approach offers an additional layer of privacy by obfuscating the source of each transaction. Secondly, we depend on regulatory agencies to oversee the compliance of blockchain transactions. This ensures that our protocol aligns with regulatory requirements while maintaining user privacy. The resulting protocol offers superior privacy protection for user transactions, with provable security and computational efficiency.

Driven by the vision of the Internet of Things (IoT) under the fifth-generation (5G) wireless network, computing resource trading attracts numerous attention from both academia and industry. Prior works mainly focus on the design of auction mechanisms to implement pricing and resource allocation. However, it is still a challenging problem because of the following three aspects: 1) How to ensure that the auction mechanism runs fairly? An auction mechanism is vulnerable and questionable since the auctioneer may fail the orders matching operation or collude with a few peers. 2) It's hard to assign the computing resources of providers to customers and guarantee reasonable rewards for each participator. 3) How to make bidding strategies for each participator? Each participator has its willingness to selWuy, which are time-variant and private. To address the above issues, we build a blockchain-enabled computing resource trading system that takes both pricing and bidding strategies into consideration, on which providers and customers can trade computing resources securely, safely and willingly. Next, we formulate a decision-making problem in the continuous double auction (CDA) to maximize their payoffs. Then, we propose a universal model-free Deep Reinforcement Learning (DRL) framework for both computing resource providers and customers. We conduct extensive experiments to evaluate the performance of our DRL framework. Simulation results show that our solution outperforms others in both static and dynamic scenarios. Our DRL framework can achieve higher rewards than others by at least 35%. Furthermore, the average trading price from our DRL framework is less volatile than that from the compared methods. The DRL framework promotes trading and brings larger trading quantities, thus resulting in higher social welfare by at least 25% than the compared schemes.

Abstract Blind signature is a crucial technique to provide anonymity in many information systems such as e‐cash, e‐voting, and smart grid systems. Partially blind signature is a more applicable extension where the part of the message includes some common information known by the signer and the signature requestor. In the family of lattice‐based schemes, blind signatures are given in ASIACRYPT 2010 by R ckert in the random oracle model, and until now, no partially blind signatures are given. We here design the first scheme based on Lyubashevsky's signature scheme in EUROCRYPT 2012 and Abe and Okamoto's construction of partially blind signature in CRYPTO 2000 in the random oracle model. The scheme shows an alternative approach to achieve the blindness property without the supports of a commitment scheme and of a final round communication to confirm the validity of a signature. Copyright © 2016 John Wiley & Sons, Ltd.

The increasing availability of cloud computing allows more and more mobile devices to outsource expensive computations. Among these computations, bilinear pairing is very fundamental and frequently-used by many modern cryptographic protocols. Currently, the most efficient outsourcing algorithm of bilinear pairings requires about 5 point additions in G1 and G2 and 4 multiplications in GT under the one-malicious version of a two-untrusted-program assumption. And the result of the algorithm is checkable with a probability about 1/2. In this paper, we improve the state-of-the-art by proposing two new outsourcing algorithms for bilinear pairings. One is a more efficient outsourcing algorithm under the same assumption with the same checkability. The other is more flexible under a two-untrusted-program assumption with improved checkability. Both algorithms are better suited to various applications where on-line computations are strictly limited due to the lack of available computing resources.

This study shows how to use signature schemes for deniable authentication. Deniable authentication means that a message receiver R, although receiving a message m from a sender S, cannot convince a third party that the sender S has sent R the message m. If signature schemes are secure against selective forgery, the authors can construct a full deniable authentication protocol. The protocol is presented in the extension framework of Raimondo et al. as a deniable message transmission authenticator.

We present an attack to current interface copy protection systems and a new framework to restrict contents flowing within legal devices. The attack is named as interface replacement attack, which is motivated by the fact that a damaged interface usually can be replaced with a small cost. The new framework deploys security mechanisms in both interior part of a consumer electronic device and interfaces on it. We name it as deep copy protection framework to distinguish the current interface-centric copy protection frameworks. We believe that this viewpoint is novel and can be further studied to develop new copy protection system.