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Transaction data on the public chain is open and transparent to all participants, which poses a potential threat to the privacy of participants. Some privacy‐conscious token holders want to employ obfuscation methods to protect the origin and destination of their tokens, and the need for token shuffling services (TSS) arose at a historical moment. The prevailing token shuffling policies rely too heavily on blacklists in the token shuffling process, which is contrary to the idea of decentralization. Therefore, weakening or even eliminating the usage of blacklisting mechanisms in TSSs, which is an urgent issue to be addressed. In this paper, we adopt the idea of machine learning and propose a general framework for TSSs, which replaces the natural role under incomplete information with machine learning, so as to achieve the goal of eliminating the blacklist mechanism in TSSs. Then, the token shuffling process is constructed as an extended game under incomplete information on the basis of different token shuffling policies, and this incomplete information game is analyzed on the basis of poison policy, haircut policy, and suicide policy respectively. Finally, the sequential equilibrium under different games is investigated through simulations. The simulation results show that in the incomplete information game based on the poison policy, the participation of two players in the TSS is a sequential equilibrium, while in the incomplete information game based on the haircut policy, the players do not participate in the TSS is an sequential equilibrium. [ABSTRACT FROM AUTHOR]

With the rapid increase of users, Scratch, as a popular online social and programming platform, has accumulated massive project resources and complex relations across its social and programming learning network. However, it is challenging to utilize the network information for providing Scratch users with personalized services, despite there are already some useful network representation learning models. In this paper, a network representation learning model with preference‐based generative adversarial nets for Scratch (ScratchGAN) is proposed to resolve this problem. In ScratchGAN, we first design a node‐vector initialization approach to preserve structure information and side information of Scratch network. Then, considering to learn the fine‐grained user preference information of network, we propose a novel Scratch adversarial learning model which includes a Scratch generative adversarial net and a user preference difference constraint component. The former aims to capture user preferences through a new generating strategy based on the delivery nature of preference. The latter attempts to embed users' detailed preference differences according to their interaction behaviors. ScratchGAN can mine user preferences while preserving network structure information and side information. Extensive experiments on the Scratch network show that ScratchGAN outperforms other state‐of‐the‐art models in link prediction and recommendation tasks. [ABSTRACT FROM AUTHOR]

vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad coins to be registered in the blacklist in time, users can only obtain part of the blacklist information, which allows illegal criminals to take advantage of it. How to prevent illegal activities under the partial information blacklist and how to design coins' quality updating rule rationally have become open issues in mixing coins. The updating rule of coins' quality in mixing is addressed since illegal criminals may carry out illegal activities, for example, money laundering. ImpSuic, an improved suicide strategy, is proposed as a new quality updating rule. The intuition is: all coins of the one who has the highest bad coins according to the blacklist, are recorded as bad coins. On the other hand, the coins' quality of others remain unchanged. Besides, linear programming is introduced into ImpSuic strategy to predict the maximum utility after mixing coins, which facilitates users to make reasonable decisions before mixing coins. Simulation results show that the quality updating rule in ImpSuic strategy can preserve users' privacy and antimoney launder. [ABSTRACT FROM AUTHOR]

Dermatopontin (DPT), a component of the extracellular matrix, plays important roles in cell-matrix interactions and matrix assembly. Some studies have revealed that it has more general functions in biological activities. However, its function in molluscs is poorly understood. In this study, a molluscan DPT gene, saDPT2, was cloned from small abalone Haliotis diversicolor. The full-length cDNA of saDPT2 sequence is 620 bp, with a 531 bp open reading frame encoding a protein of 177 amino acids (aa). Amino acid sequence analysis revealed that saDPT2 shares conserved signature motifs with other DPT proteins, including three repeats of 10-residue motif (S-X-H-X-N-X-Y-E-D-R), which is similar to mammalian 6-residue repeating sequence of D-R-E/Q-W-X-F/Y. Quantitative real-time PCR was employed to investigate the tissue distribution of saDPT2 mRNA, its expression at different developmental stages, and in abalone under bacteria challenge. The saDPT2 mRNA could be detected in all examined tissues and developmental stages. Moreover, the saDPT2 mRNA was up-regulated in haemocytes and gills after bacteria injection. The results indicate that the saDPT2 could respond to pathogenic infection and may play a role in adult abalone immune system. [ABSTRACT FROM AUTHOR]

ABSTRACT Rational secure two-party computation (RSTPC) applies game theory to cryptographic protocols, where rational parties only care about how to maximize their utilities. Fairness can be achieved by assuming rational behavior in RSTPC. Previous RSTPC protocols achieve fairness under symmetric information, which seems quite strong and unnatural for rational parties. In this paper, we consider a general RSTPC protocol under asymmetric information where previous equilibriums are inadequate to guarantee fairness. Therefore, we propose a stronger equilibrium named computationally sequential equilibrium to guarantee fairness under asymmetric information. Furthermore, our protocol only requires small constant communication rounds. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, we redefine the utilities considering other parts such as reputation in order to give rational parties incentives to participate in the protocol and finally get the correct outputs. The most distinct contribution of this paper is the proposal of rational covert adversaries whose behaviors are combinations of rational parties and covert adversaries. Finally, given proper parameters, we prove that all parties have incentives to participate in the rational secure computation protocol. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A correction to the article "New Rational Parties Relying on Reputation" is presented that was published in the 2014 issue of the periodical.