The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform

Qingquan He - One of the best experts on this subject based on the ideXlab platform.

  • E-commerce seller credit comprehensive evaluation model
    2011 International Conference on Computer Science and Service System (CSSS), 2011
    Co-Authors: Fang Liang, Qingquan He
    Abstract:

    This paper analyzes the problems of the main credit evaluation model and established an improved comprehensive evaluation model for C2C e-commerce credit; The improved model is based on the main indicators of the current credit evaluation: product quality, logistics, and service attitude, and comprehensive considerate the impact weights of various indicators; At the same time, We calculate the user's credit with the counterparty creditworthiness number of Transactions and Transaction Amount. For new users, We places the user's level of social credit in place of its initial credit status, and then determine the user's credit rating assessment. This model used to solve the major problem which emerges in current C2C site evaluation model.

  • C2C E-Commerce Credit Comprehensive Evaluation Model for Multi-Factors
    2010 International Conference on Internet Technology and Applications, 2010
    Co-Authors: Qingquan He
    Abstract:

    This paper analyzes the problems of the main credit evaluation model,and establishs an improved comprehensive evaluation model for C2C e-commerce credit; The improved model is based on the main indicators of the current credit evaluationt: product quality, logistics, and service attitude ,and comprehensive considerates the impact weights of various indicators ; At the same time , We calculate the user's credit with the counterparty creditworthiness number of Transactions and Transaction Amount . For new users, We places the user's level of social credit in place of its initial credit status, and then determine the user's credit rating assessment. This model used to solve the major problems which emerges in current C2C site evaluation model.

  • C2C E-Commerce Credit Comprehensive Evaluation Model for Multi-Factors
    2010 International Conference on Internet Technology and Applications, 2010
    Co-Authors: Qingquan He
    Abstract:

    This paper analyzes the problems of the main credit evaluation model, and establishes an improved comprehensive evaluation model for C2C e-commerce credit. The improved model is based on the main indicators of the current credit evaluation: product quality, logistics, and service attitude, and comprehensive considerates the impact weights of various indicators. At the same time, we calculate the user's credit with the counterparty creditworthiness number of Transactions and Transaction Amount . For new users, we place the user's level of social credit in place of its initial credit status, and then determine the user's credit rating assessment. This model used to solve the major problems which emerges in current C2C site evaluation model.

Prateek Saxena - One of the best experts on this subject based on the ideXlab platform.

  • ESORICS (2) - A Traceability Analysis of Monero’s Blockchain
    Computer Security – ESORICS 2017, 2017
    Co-Authors: Amrit Kumar, Clément Fischer, Shruti Tople, Prateek Saxena
    Abstract:

    Privacy and anonymity are important desiderata in the use of cryptocurrencies. Monero—a privacy centric cryptocurrency has rapidly gained popularity due to its unlinkability and untraceablity guarantees. It has a market capitalization of USD 290M. In this work, we quantify the efficacy of three attacks on Monero’s untraceability guarantee, which promises to make it hard to trace the origin of a received fund, by analyzing its blockchain data. To this end, we develop three attack routines and evaluate them on the Monero blockchain. Our results show that in 88% of cases, the origin of the funds can be easily determined with certainty. Moreover, we have compelling evidence that two of the attack routines also extend to Monero RingCTs—the second generation Monero that even hides the Transaction Amount. We further observe that over 98% of the results can in fact be obtained by a simple temporal analysis. In light of our findings, we discuss mitigations to strengthen Monero against these attacks. We shared our findings with the Monero development team and the general community. This has resulted into several discussions and proposals for fixes.

  • a traceability analysis of monero s blockchain
    European Symposium on Research in Computer Security, 2017
    Co-Authors: Amrit Kumar, Clément Fischer, Shruti Tople, Prateek Saxena
    Abstract:

    Privacy and anonymity are important desiderata in the use of cryptocurrencies. Monero—a privacy centric cryptocurrency has rapidly gained popularity due to its unlinkability and untraceablity guarantees. It has a market capitalization of USD 290M. In this work, we quantify the efficacy of three attacks on Monero’s untraceability guarantee, which promises to make it hard to trace the origin of a received fund, by analyzing its blockchain data. To this end, we develop three attack routines and evaluate them on the Monero blockchain. Our results show that in 88% of cases, the origin of the funds can be easily determined with certainty. Moreover, we have compelling evidence that two of the attack routines also extend to Monero RingCTs—the second generation Monero that even hides the Transaction Amount. We further observe that over 98% of the results can in fact be obtained by a simple temporal analysis. In light of our findings, we discuss mitigations to strengthen Monero against these attacks. We shared our findings with the Monero development team and the general community. This has resulted into several discussions and proposals for fixes.

  • A Traceability Analysis of Monero’s Blockchain
    Computer Security – ESORICS 2017, 2017
    Co-Authors: Amrit Kumar, Clément Fischer, Shruti Tople, Prateek Saxena
    Abstract:

    Privacy and anonymity are important desiderata in the use of cryptocurrencies. Monero—a privacy centric cryptocurrency has rapidly gained popularity due to its unlinkability and untraceablity guarantees. It has a market capitalization of USD 290M. In this work, we quantify the efficacy of three attacks on Monero’s untraceability guarantee, which promises to make it hard to trace the origin of a received fund, by analyzing its blockchain data. To this end, we develop three attack routines and evaluate them on the Monero blockchain. Our results show that in 88% of cases, the origin of the funds can be easily determined with certainty. Moreover, we have compelling evidence that two of the attack routines also extend to Monero RingCTs—the second generation Monero that even hides the Transaction Amount. We further observe that over 98% of the results can in fact be obtained by a simple temporal analysis. In light of our findings, we discuss mitigations to strengthen Monero against these attacks. We shared our findings with the Monero development team and the general community. This has resulted into several discussions and proposals for fixes.

Noboru Kunihiro - One of the best experts on this subject based on the ideXlab platform.

  • ISITA - Decentralized Netting Protocol over Consortium Blockchain
    2018 International Symposium on Information Theory and Its Applications (ISITA), 2018
    Co-Authors: Ken Naganuma, Masayuki Yoshino, Hisayoshi Sato, Nishio Yamada, Takayuki Suzuki, Noboru Kunihiro
    Abstract:

    In recent years, Bitcoin, Ethereum and other cryptocurrencies have attracted a great deal of attention from the whole industry including the financial as a new settlement system. Transaction information of these cryptocurrencies is stored in a distribution ledger called Blockchain on the P2P network through processing such as PoW. Meanwhile, since PoW requires a large Amount of computer resources, researches on private / consortium type blockchain that do not need PoW. In this paper, we propose a decentralized netting protocol using a consortium type block chain that has the channel function. On a system that implements the proposed protocol, netting settlement can be performed on P2P hiding information of the sender and receiver name of Transaction, Amount of money, calculation butt of netting, and without setting up a specific central organization such as a central server.

  • Decentralized Netting Protocol over Consortium Blockchain
    2018 International Symposium on Information Theory and Its Applications (ISITA), 2018
    Co-Authors: Ken Naganuma, Masayuki Yoshino, Hisayoshi Sato, Nishio Yamada, Takayuki Suzuki, Noboru Kunihiro
    Abstract:

    In recent years, Bitcoin, Ethereum and other cryptocurrencies have attracted a great deal of attention from the whole industry including the financial as a new settlement system. Transaction information of these cryptocurrencies is stored in a distribution ledger called Blockchain on the P2P network through processing such as PoW. Meanwhile, since PoW requires a large Amount of computer resources, researches on private / consortium type blockchain that do not need PoW. In this paper, we propose a decentralized netting protocol using a consortium type block chain that has the channel function. On a system that implements the proposed protocol, netting settlement can be performed on P2P hiding information of the sender and receiver name of Transaction, Amount of money, calculation butt of netting, and without setting up a specific central organization such as a central server.

Yinghui Xu - One of the best experts on this subject based on the ideXlab platform.

  • KDD - Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
    Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
    Co-Authors: Yujing Hu, Qing Da, An-xiang Zeng, Yang Yu, Yinghui Xu
    Abstract:

    In E-commerce platforms such as Amazon and TaoBao , ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems. However, such methods often consider different ranking steps in a session to be independent, which conversely may be highly correlated to each other. For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session. Firstly, we formally define the concept of search session Markov decision process (SSMDP) to formulate the multi-step ranking problem. Secondly, we analyze the property of SSMDP and theoretically prove the necessity of maximizing accumulative rewards. Lastly, we propose a novel policy gradient algorithm for learning an optimal ranking policy, which is able to deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP. Experiments are conducted in simulation and TaoBao search engine. The results demonstrate that our algorithm performs much better than the state-of-the-art LTR methods, with more than 40% and 30% growth of total Transaction Amount in the simulation and the real application, respectively.

  • Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
    arXiv: Learning, 2018
    Co-Authors: Yujing Hu, Qing Da, An-xiang Zeng, Yang Yu, Yinghui Xu
    Abstract:

    In e-commerce platforms such as Amazon and TaoBao, ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems. However, such methods often consider different ranking steps in a session to be independent, which conversely may be highly correlated to each other. For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session. Firstly, we formally define the concept of search session Markov decision process (SSMDP) to formulate the multi-step ranking problem. Secondly, we analyze the property of SSMDP and theoretically prove the necessity of maximizing accumulative rewards. Lastly, we propose a novel policy gradient algorithm for learning an optimal ranking policy, which is able to deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP. Experiments are conducted in simulation and TaoBao search engine. The results demonstrate that our algorithm performs much better than online LTR methods, with more than 40% and 30% growth of total Transaction Amount in the simulation and the real application, respectively.

Nadra Guizani - One of the best experts on this subject based on the ideXlab platform.

  • Toward Privacy and Regulation in Blockchain-Based Cryptocurrencies
    IEEE Network, 2019
    Co-Authors: Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Xiaojiang Du, Nadra Guizani
    Abstract:

    Privacy is supreme in cryptocurrencies since most users do not want to reveal their identities or the Transaction Amount in financial Transactions. Nevertheless, achieving privacy in blockchain-based cryptocurrencies remains challenging since blockchain is by default a public ledger. For instance, Bitcoin provides builtin pseudonymity rather than true anonymity, which can be compromised by analyzing the Transactions. Several solutions have been proposed to enhance the Transaction privacy of Bitcoin. Unfortunately, full anonymity is not always desirable, because malicious users are able to conduct illegal Transactions, such as money laundering and drug trading, under the cover of anonymity in cryptocurrencies. As a result, regulation in blockchain-based cryptocurrencies is very essential. In this article, we analyze the privacy issues in Bitcoin and investigate some existing privacy-enhancing techniques in blockchain- based cryptocurrencies as well as some privacy-focused altcoins. In addition, we review and compare some works dealing with regulation of cryptocurrencies. Finally, we propose two possible solutions from a top view to balance privacy and regulation of blockchain-based cryptocurrencies. One solution is based on decentralized group signature, in which a group manager is responsible for building a group and tracing the real payer of the group in a Transaction. The other solution is based on verifiable encryption, in which a tracing manager is not actively involved in normal Transactions but can trace suspicious Transactions via an encrypted tag.