Privacy-preserving Data Processing at Scale: How Much Can You Trust Your Cloud Provider?
Pascal Felber, Universite de Neuchatel, Switzerland
Trustworthy Federated Learning Systems
Lydia Chen, Technology University Delft, Netherlands
Serverless Across the Edge-Cloud Continuum: Issues and Perspectives
Valeria Cardellini, University of Rome Tor Vergata, Italy
Privacy-preserving Data Processing at Scale: How Much Can You Trust Your Cloud Provider?
Pascal Felber
Universite de Neuchatel
Switzerland
Brief Bio
Pascal Felber received his M.Sc. and Ph.D. degrees in Computer Science from the Swiss Federal Institute of Technology. From 1998 to 2002, he has worked at Oracle Corporation and Bell-Labs (Lucent Technologies) in the USA. From 2002 to 2004, he has been an Assistant Professor at Institut EURECOM in France. Since October 2004, he is a Professor of Computer Science at the University of Neuchâtel, Switzerland, working in the field of dependable, concurrent, and distributed computing. He has published over 200 research papers in various journals and conferences.
Abstract
The processing of large amounts of data requires significant computing power and scalable architectures. This trend makes the use of Cloud computing and off-premises data centres particularly attractive but exposes companies to the risk of data theft. This is a key challenge toward outsourcing data processing to external Cloud providers, as data represents for many companies their most valuable asset. In this talk, we will discuss recent and emerging mechanisms to support privacy-preserving data processing, i.e., confidential computing, on untrusted architectures.
Trustworthy Federated Learning Systems
Lydia Chen
Technology University Delft
Netherlands
Brief Bio
Lydia Y. Chen is a Professor in the Department of Computer Science at the University of Neuchatel in Switzerland and Delft University of Technology in the Netherlands. Prior to joining TU Delft, she was a research staff member at the IBM Research Zurich Lab from 2007 to 2018. She holds a PhD from Pennsylvania State University and a BA from National Taiwan University. Her research interests are distributed machine learning, dependability management, large-scale data processing systems and services. More specifically, her work focuses on developing machine learning and stochastic models, and applying these techniques to application domains, such as data centers and AI systems.
She has published more than 100 papers in peer-reviewed journals and serves on the technical program committees of system and AI conferences and the editorial boards on multiple of IEEE Transactions journals.
Abstract
Federated learning (FL) is an emerging collaborative learning paradigm which enables data owners to extract knowledge and learn models jointly. FL empowers the model democracy by inviting the contribution of crowd and protects the data privacy by keeping the data on premise. In this talk, I will first discuss its worthiness and technical challenges on training diversified learning models, from classification, graph, to generative ones. Through concrete examples, I will then demonstrate the vulnerability issues stemming from the malicious crowd, covering poisoning attacks, freerider attacks, and data reconstruction attacks. I will conclude this talk with a discussion on the defense strategies to baffle adversaries and strengthen the trust of FL.
Serverless Across the Edge-Cloud Continuum: Issues and Perspectives
Valeria Cardellini
University of Rome Tor Vergata
Italy
Brief Bio
Valeria Cardellini is a full professor of computer science in the Department of Civil Engineering and Computer Science Engineering at the University of Rome Tor Vergata, Italy. She received her PhD degree in computer science from the University of Rome Tor Vergata in 2001. Her research interests are in the field of distributed software systems, including Cloud and Edge systems and services, resource management and self-adaptation, quality assurance. She has co-authored more than 100 publications in international journals and conferences and serves on the editorial boards of IEEE TPDS and Elsevier JPDC and on the technical program committees of system and performance conferences.
Abstract
Serverless computing and the Function-as-a-Service (FaaS) paradigm have enjoyed growing popularity over the last years. The convergence of this paradigm and the Edge-Cloud compute continuum, characterized by a largely heterogeneous environment, brings along a new set of research issues and challenges. They include, among others, scheduling, offloading, and energy awareness, as we will discuss during the talk. As a case study, we will discuss how we tackle these challenges in Serverledge, a decentralized open-source FaaS platform designed for the Edge-Cloud continuum.