Trustworthy Distributed Consensus and Verifiable Computation
Consensus in distributed systems, agreeing on a global state under synchronous or asynchronous communication, is a challenging and costly problem. As assumed for blockchain or other distributed ledger technology, the problem increases under security assumptions, i.e., curious-but-honest participants or Byzantine-failure model. Here, the full replication of data leads to a significant resource burden. Sharding has been studied to reduce these costs, but it leads to security, liveness, and safety issues. The talk will present a line of work around liveness and safety properties for secure sharding algorithms and will touch upon privacy-preserving computation for verifiable decentralized programs.
Dr. Boris Düdder is Associate Professor of Formal Methods in Software Engineering at the Department of Computer Science at the University of Copenhagen, Denmark. He is the Director of the Laboratory for Trustworthy AI and Head of the Research Group Software Engineering & Formal Methods. His research interests are in formal methods and programming languages for software engineering of safe and dependable distributed systems. He is a guest lecturer at the Copenhagen Business School (CBS), Denmark, and the University of Electronic Science and Technology of China (UESTC), Chengdu, P.R. China. He is involved in multiple projects on innovative and dependable industrial IT infrastructure for enterprises, manufacturing industries, and national healthcare IT, e.g., Data Ecosystems, Smart Factories, and Industry 4.0. He received several international, European, and national research grants on scalable infrastructures for data ecosystems, healthcare IT, distributed ledger technologies, and FinTech. He has published over 40 papers in top international journals and conferences. His academic and industrial background, e.g., TU Dortmund, Fraunhofer, Germany, and Microsoft, USA.
Exploring Data Collection, Selling, and Privacy in Data Markets
In recent years, with the rapid development of the big data era, there has been a continuous increase in the volume of data generated and collected in the digital world. This data encompasses various sources such as social media, e-commerce, and the Internet of Things. Big data has become integral to many industries, where companies and organizations leverage it to gain insights, improve operations, and make data-driven decisions. Data markets have emerged to facilitate data circulation and effectively harness the value hidden within data. This talk focuses on exploring the issues related to data collection and selling within the context of data markets while considering data privacy and security. It showcases several thoughtfully designed frameworks and systems, each tailored to achieve specific goals. The primary focus is to provide diverse solutions for overcoming the challenges related to data collection and selling in data markets, with a strong emphasis on safeguarding data privacy and security.
Xikun Jiang is a postdoctoral researcher in the Software, Data, People, and Society (SDPS) section at the University of Copenhagen, Denmark. She is driven by a deep passion for conducting cutting-edge research at the intersection of data management, network economics, and Machine Learning. Xikun earned her PhD degree in Computer Science from Shanghai Jiao Tong University, China. Currently, she works closely with Associate Professor Boris Düdder in the SDPS section, dedicating her research efforts to advancing the domain of data management. She leverages economic models and state-of-the-art Machine Learning techniques to extract valuable and meaningful insights from complex datasets.