Search results

Handbook of Blockchain, Digital Finance, and Inclusion, Volume 2: ChinaTech, Mobile Security, and Distributed Ledger emphasizes technological developments that introduce the future of finance. Descriptions of recent innovations lay the foundations for explorations of feasible solutions for banks and startups to grow. The combination of studies on blockchain technologies and applications, regional financial inclusion movements, advances in Chinese finance, and security issues delivers a grand perspective on both changing industries and lifestyles. Written for students and practitioners, it helps lead the way to future possibilities.

Title page -- Table of Contents -- Copyright -- Dedication -- List of Contributors -- Preface -- Background -- Purpose -- Themes -- Acknowledgment by David LEE Kuo Chuen -- Acknowledgment by Robert Deng -- Chapter 1: The Game of Dian Fu: The Rise of Chinese Finance -- Abstract -- Acknowledgments -- 1.1. Introduction: What Is Dianfu ? -- 1.2. Dian Fu One: Dian Fu in the Equity Market -- 1.3. Dian Fu Two: Dian Fu in China's Peer-to-Peer Lending -- 1.4. Dian Fu Three: Dian Fu in Crowdfunding -- 1.5. Dian Fu Four: Dian Fu in the People's Currency -- 1.6. Dian Fu Five: Dian Fu in Banking -- 1.7. Conclusion -- Appendix: Acquisitions of Alibaba -- References -- Chapter 2: Balancing Innovation and Risks in Digital Financial Inclusion-Experiences of Ant Financial Services Group -- Abstract -- 2.1. Introduction -- 2.2. Definition -- 2.3. Experiences of Ant Financial -- References -- Chapter 3: Regulating FinTech in China: From Permissive to Balanced -- Abstract -- 3.1. Introduction -- 3.2. The Evolution of Digital Financial Services in China -- 3.3. Regulation of Digital Financial Services in China -- 3.4. Conclusion -- Chapter 4: Big Data Technology: Application and Cases -- Abstract -- 4.1. Why Big Data Technology Matters? -- 4.2. How Big Data Technology Helps Improving Credit Performance -- 4.3. Brief Introduction of CreditEase: Better Technology, Better Finance -- 4.4. An Instance of Big Data Technology: CreditEase Financial Cloud -- 4.5. Cases and Application Scenarios for CreditEase Financial Cloud -- References -- Chapter 5: Trust Management in Mobile Platforms -- Abstract -- Acknowledgments -- 5.1. Introduction -- 5.2. Trust, Trust Modeling, and Trust Management -- 5.3. Trust Management in Mobile Platforms -- 5.4. Further Discussions -- 5.5. Conclusions -- References -- Chapter 6: Security Issues of In-Store Mobile Payment -- Abstract

It is commonly acknowledged that customers' privacy in electronic commerce should be well protected. The solutions may come not only from the ethics education and legislation, but also from cryptographic technologies. In this paper we propose and analyze a privacy protection scheme for e-commerce of digital goods. The scheme takes cryptography as its technical means to realize privacy protection for online customers. It is efficient in both computational cost and communication cost. It is very practical for real e-commerce systems compared with previous solutions. The cryptographic technique presented in this paper is rather simple. But the scheme has great application potential in reality. We give careful security analysis to the scheme.

Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep learning has been explored in a wide variety of fields and produced unparalleled performance results. It plays a vital role in daily applications and is also subtly changing the rules, habits, and behaviors of society. However, inevitably, data-based learning strategies are bound to cause potential security and privacy threats, and arouse public as well as government concerns about its promotion to the real world. In this article, we mainly focus on data security issues in deep learning. We first investigate the potential threats of deep learning in this area, and then present the latest countermeasures based on various underlying technologies, where the challenges and research opportunities on offense and defense are also discussed. Then, we propose SecureNet, the first verifiable and privacy-preserving prediction protocol to protect model integrity and user privacy in DNNs. It can significantly resist various security and privacy threats during the prediction process. We simulate SecureNet under a real dataset, and the experimental results show the superior performance of SecureNet for detecting various integrity attacks against DNN models.