The financial sector’s adoption of technology-driven data analysis has enhanced operational efficiency and revenue generation by leveraging personal sensitive data. However, the inherent characteristics of blockchain hinder decentralized finance (DeFi) from accessing necessary sensitive user data. To address this challenge, we introduce a protocol that both safeguards user privacy and ensures data availability through the incorporation of homomorphic encryption and zero-knowledge-proof techniques in blockchain technology. This novel protocol helps mitigate privacy risks caused by sensitive data leaks while improving the capital efficiency of the DeFi market. Furthermore, we explore the applicability of these privacy-preserving methods in on-chain ecosystems and cross-border financial applications. Our solution contributes to secure, user-centric solutions for DeFi while upholding principles of decentralization and privacy protection.

Throughout the ages, social change has largely been brought as a reaction against an establishing entity or idea. Communism, for instance, was a reaction against the industrial revolution that exacerbated the exploitative nature of capitalism. Contemporarily, we are living through perhaps one of the most critical turning points in finance that began as a reaction against the solipsistic nature of Wall Street bankers prior to the 2008 financial crisis. This new movement exists within the robust ecosystem of decentralized finance (DeFi). Throughout the past decade, innovation within DeFi has grown exponentially, such that literature on its social, political, and economic effects is yet to be well-understood. Thus, this research paper intends to deconstruct the underlying socio-economic systems surrounding DeFi in order to understand, analyze,and critique its fundamental values and assumptions. The primary philosophical concern is whether decentralized finance can be a suitable substitute to the current financial system via its notions of personal financial freedom.

With rapid development of blockchain technology as well as integration of various application areas, performance evaluation, performance optimization, and dynamic decision in blockchain systems are playing an increasingly important role in developing new blockchain technology. This paper provides a recent systematic overview of this class of research, and especially, developing mathematical modeling and basic theory of blockchain systems. Important examples include (a) performance evaluation: Markov processes, queuing theory, Markov reward processes, random walks, fluid and diffusion approximations, and martingale theory; (b) performance optimization:Linear programming, nonlinear programming, integer programming, and multi-objective programming; (c) optimal control and dynamic decision: Markov decision processes, and stochastic optimal control; and (d) artificial intelligence: Machine learning, deep reinforcement learning, and federated learning. So far, a little research has focused on these research lines. We believe that the basic theory with mathematical methods, algorithms and simulations of blockchain systems discussed in this paper will stronglysupport future development and continuous innovation of blockchain technology.