ISSN 1000-1239 CN 11-1777/TP



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    Journal of Computer Research and Development    2021, 58 (2): 235-236.   DOI: 10.7544/issn1000-1239.2021.qy0201
    Abstract429)   HTML9)    PDF (192KB)(274)       Save
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    Blockchain-Based Data Transparency: Issues and Challenges
    Meng Xiaofeng, Liu Lixin
    Journal of Computer Research and Development    2021, 58 (2): 237-252.   DOI: 10.7544/issn1000-1239.2021.20200017
    Abstract1323)   HTML28)    PDF (1812KB)(996)       Save
    With the high-speed development of Internet of things, wearable devices and mobile communication technology, large-scale data continuously generate and converge to multiple data collectors, which influences people’s life in many ways. Meanwhile, it also causes more and more severe privacy leaks. Traditional privacy aware mechanisms such as differential privacy, encryption and anonymization are not enough to deal with the serious situation. What is more, the data convergence leads to data monopoly which hinders the realization of the big data value seriously. Besides, tampered data, single point failure in data quality management and so on may cause untrustworthy data-driven decision-making. How to use big data correctly has become an important issue. For those reasons, we propose the data transparency, aiming to provide solution for the correct use of big data. Blockchain originated from digital currency has the characteristics of decentralization, transparency and immutability, and it provides an accountable and secure solution for data transparency. In this paper, we first propose the definition and research dimension of the data transparency from the perspective of big data life cycle, and we also analyze and summary the methods to realize data transparency. Then, we summary the research progress of blockchain-based data transparency. Finally, we analyze the challenges that may arise in the process of blockchain-based data transparency.
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    Ethical Behavior Discrimination Based on Social News Dataset
    Gu Tianlong, Feng Xuan, Li Long, Bao Xuguang, Li Yunhui
    Journal of Computer Research and Development    2021, 58 (2): 253-263.   DOI: 10.7544/issn1000-1239.2021.20200727
    Abstract553)   HTML15)    PDF (979KB)(346)       Save
    With the broader applications of artificial intelligence (AI), their ethical and moral issues have attracted more and more concerns. How to develop an AI system that complies with human values and ethical norms from the perspective of technology realization, namely, ethical aligned AI design, is one of the important issues that need to be solved urgently. The ethical and moral discrimination based on machine learning is a beneficial exploration in this aspect. Social news data has rich content and knowledge of ethics and morality, which provides the possibility for the training data development of machine learning. Because of this, this paper constructs a social news dataset with ethics and morality of human behavior, which is attached to law and code of conduct dataset for machine learning training and testing. The ethical behavior discrimination model ERNIE-CNN based on enhanced language representation of information entities (ERNIE) and convolutional neural network (CNN), is developed to extract ethical discriminations about behavior by calculating semantic similarity based on the vector representation of words. The experimental results show that the proposed model has better performance than the baseline models.
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    Fairness Research on Deep Learning
    Chen Jinyin, Chen Yipeng, Chen Yiming, Zheng Haibin, Ji Shouling, Shi Jie, Cheng Yao
    Journal of Computer Research and Development    2021, 58 (2): 264-280.   DOI: 10.7544/issn1000-1239.2021.20200758
    Abstract1014)   HTML26)    PDF (1752KB)(844)       Save
    Deep learning is an important field of machine learning research, which is widely used in industry for its powerful feature extraction capabilities and advanced performance in many applications. However, due to the bias in training data labeling and model design, research shows that deep learning may aggravate human bias and discrimination in some applications, which results in unfairness during the decision-making process, thereby will cause negative impact to both individuals and socials. To improve the reliability of deep learning and promote its development in the field of fairness, we review the sources of bias in deep learning, debiasing methods for different types biases, fairness measure metrics for measuring the effect of debiasing, and current popular debiasing platforms, based on the existing research work. In the end we explore the open issues in existing fairness research field and future development trends.
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    A Secure Multi-Party Computation Protocol for Universal Data Privacy Protection Based on Blockchain
    Liu Feng, Yang Jie, Li Zhibin, Qi Jiayin
    Journal of Computer Research and Development    2021, 58 (2): 281-290.   DOI: 10.7544/issn1000-1239.2021.20200751
    Abstract905)   HTML39)    PDF (1496KB)(768)       Save
    Recent years, how to protect user privacy data on the blockchain reasonably and efficiently is a key issue in the current blockchain technology field. Based on this, in this paper, a secure multi-party computation protocol is designed based on the Pedersen commitment and Schnorr protocol (protocol of blockchain based on Pedersen commitment linked schnorr protocol for multi-party computation, BPLSM). Through constructing the structure of the protocol and carrying out formal proof calculations, it is confirmed that the protocol can be integrated into the blockchain network to merge different private messages for efficient signing under anonymity. In addition, by analyzing the nature and security of the protocol, it can be proved that the overhead about computation of the general-purpose privacy computing scheme using the BPLSM protocol on the blockchain is low, and it also has strong information imperceptibility. In the end, experimental simulation results show that the time cost of BPLSM protocol verification in a small-scale multi-party transaction with a fixed number of people is about 83.5% lower than that of the current mainstream BLS signature.
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