Abstract: In the rapidly evolving landscape of blockchain and decentralized finance (DeFi), the analysis of onchain data has emerged as a critical tool for understanding and optimizing digital ecosystems. This talk will provide an overview of onchain data analytics, highlighting its significance in driving transparency, security, and efficiency within decentralized networks. The talk will explore how onchain data analytics sheds insight into the decentralization of token distribution from an asset perspective. By examining case studies, we will delve into practical applications where onchain analytics has played a pivotal role in early fraud detection, identifying malicious activities before they can compromise the integrity of the network. Additionally, we will discuss how intention analysis, powered by onchain data, can provide valuable insights into user behavior, aiding in the development of more secure and user-centric blockchain applications.

Bio: Dr. Feida Zhu is an associate professor and the associate dean of Singapore Management University. His research interests include large-scale data mining and machine learning, block chain, text mining, graph/network mining and social network analysis, with emphasis on their application to business, financial and consumer innovation. He is also the founder of Symphony, a blockchain-based platform for data-driven economy, with unique set of technology breakthrough in distributed data mining, privacy-preserving data mining, consensus protocol design and blockchain architecture. He was the General Co-Chair of SIGKDD 2021 in Singapore and the General Co-Chair of ICDM’18, one of the most successful conferences of IEEE for year 2018 with 800 attendees and over 1000 paper submissions. Feida has initiated the “ICDM Blockchain Day” which has attracted a 300-people audience, with keynote speakers including Dr. C. Mohan from IBM as well as a fleet of top practitioners from leading blockchain projects around the world.

Abstract: The advance of digital technology brings significant benefits to humanity. However, there is a growing demand for greater transparency and visibility of risks before integrating more digital technologies into daily life and delegating critical functions to AI-driven services. Convincing existing tech companies, teams, or individuals to prioritise security and regulation is unrealistic without clear benefits. One solution is to create a well-regulated but decentralised system that runs parallel to what we might consider the "free-style" digital world. It offers a new direction and ecosystem that encourages and motivates the development of regulations, transparency, and the prevention of potential risks from rogue AI or systems.

Bio: Dr. Bangdao Chen received his Bachelor degree of Computer Science from Shanghai Jiaotong University, and both his Master’s and PhD in Computer Science from University of Oxford, where he also served as postdoctoral researcher. His main research directions include decentralised identity, payment security and communication security. He currently works on research and product development of information security, blockchain and other related technology. Together with his supervisor, Professor Bill Roscoe, he co-founded The Blockhouse Technology Ltd., Oxford University College Blockchain Research Center, Oxford Hainan Blockchain Research Institute and other enterprises and research institutions.

Abstract: Decentralized Physical Infrastructure Network (DePIN) represents an innovative approach to managing physical infrastructure using blockchain technology. By 2024, the market capitalization of DePIN-related projects has surpassed $10 billion, reflecting significant popularity and investment. Despite the excitement within the Web3 community, there is a notable lack of academic research on DePIN’s foundational aspects and economic impact. This paper addresses these gaps with a comprehensive analysis of the DePIN ecosystem. We categorize various DePIN networks by economic sectors, examine the unique characteristics of DePIN data assets, and investigate the economic mechanisms of DePIN implementations. Through a detailed taxonomy, real-world data analysis, and assessment of economic incentives, this study aims to enhance the understanding of DePIN and to support its development as a sustainable and efficient model for managing physical infrastructure.

Abstract: The Bitcoin Lightning Network is a layer 2 protocol designed to facilitate fast and inexpensive Bitcoin transactions. It operates by establishing channels between users, where Bitcoin is locked and transactions are conducted off-chain until the channels are closed, with only the initial and final transactions recorded on the blockchain. Routing transactions through intermediary nodes is crucial for users without direct channels, allowing these routing nodes to collect fees for their services. Nodes announce their channels to the network, forming a graph with channels as edges. In this paper, we analyze the graph structure of the Lightning Network and investigate the statistical relationships between node properties using machine learning, particularly Graph Neural Networks (GNNs). We formulate a series of tasks to explore these relationships and provide benchmarks for GNN architectures, demonstrating how topological and neighbor information enhances performance. Our evaluation of several models reveals the effectiveness of GNNs in these tasks and highlights the insights gained from their application.

Abstract: The need for data security and model integrity has been accentuated by the rapid adoption of AI and ML in data-driven domains including healthcare, finance, and security. Large models are crucial for tasks like diagnosing diseases and forecasting finances but tend to be delicate and not very scalable. Decentralized systems solve this issue by distributing the workload and reducing central points of failure. Yet, data and processes spread across different nodes can be at risk of unauthorized access, especially when they involve sensitive information. Nesa solves these challenges with a comprehensive framework using multiple techniques to protect data and model outputs. This includes zero-knowledge proofs for secure model verification. The framework also introduces consensus-based verification checks for consistent outputs across nodes and confirms model integrity. Split Learning divides models into segments processed by different nodes for data privacy by preventing full data access at any single point. For hardware-based security, trusted execution environments are used to protect data and computations within secure zones. Nesa’s state-of-the-art proofs and principles demonstrate the framework’s effectiveness, making it a promising approach for securely democratizing artificial intelligence.

Abstract: Ensuring privacy and security while reaching consensus presents major issues in evolving decentralized systems. Traditional consensusbuilding techniques without encryption frequently compromise privacy and confidentiality by requiring participants to reveal sensitive information. Meanwhile, with the integration of Artificial Intelligence (AI) into Web3, these traditional consensuses, such as Proof of Work (PoW) and Proof of Stake (PoS) can not fit the characteristics of AI tasks anymore. To address these issues, we propose a novel approach leveraging Fully Homomorphic Encryption (FHE) to encrypt Proof of Intelligence (PoI) consensus in AI tasks. This framework guarantees data privacy with the calculations on encrypted data during the consensus procedure. Additionally, it mitigates the risk of malicious clients stealing or free-riding on others’ value within the decentralized system. By proving the security properties of correctness and ciphertext indistinguishability with FHE, our approach offers a robust solution for achieving consensus while safeguarding the integrity and confidentiality of participants’ data. To validate our framework, we demonstrate its effectiveness through empirical security analysis and performance analysis.

Panel discussion list:
1. How can AI enhance the scalability, security, and efficiency of blockchain and Web3 applications, particularly in creating a sustainable digital economy? Or the other way around, how can Web3 benefit AI for the same goal? Can you provide examples of successful implementations or ongoing projects that illustrate this integration?
2. What role do blockchain and Web3 technologies play in fostering a more sustainable and ethically responsible digital economy? How can AI contribute to addressing challenges such as energy consumption, data privacy, and fair distribution of resources within these ecosystems?
3. How can collaboration between academia and industry drive innovation in the development of AI-powered blockchain and Web3 solutions? What are the key challenges and opportunities in translating academic research into real-world applications that support a sustainable digital economy?
4. What are the emerging trends at the intersection of AI, blockchain, and Web3 that will shape the future of the digital economy? How can academic institutions and industry leaders work together to equip the next generation of professionals with the necessary skills to thrive in this evolving landscape?