Minos Garofalakis

Professor at the School of ECE of the Technical University of Crete

Minos Garofalakis is the Director of the Information Management Systems Institute (IMSI) at the ATHENA Research Center and a Professor at the School of ECE at the Technical University of Crete (TUC). He also works as a senior research consultant for Huawei ISR and is the Co-founder and Director of Research at Agora Labs, a startup company bringing state-of-the-art data privacy technologies to the healthcare domain. Minos received the MSc and PhD degrees from the University of Wisconsin-Madison, and previously held senior/principal researcher positions at Bell Labs (1998-2005), Intel Research Berkeley (2005-2007), and Yahoo! Research (2007-2008); in parallel, he held an Adjunct Professor position at the EECS Department of UC Berkeley (2006-2008). Minos’s research interests lie in the broad area of Big Data Analytics. He has published over 170 papers that have received more than 16,500 citations (h-index=70) according to Google Scholar. Minos is an ACM and IEEE Fellow, a Member of the Academia Europaea, and a recipient of several awards, including the TUC “Excellence in Research” Award (2015), the Bell Labs President’s Gold Award (2004), and two Best Research Paper Awards (VLDB’2024, ICDE’2009).

Title: Have your data and share it too: Private data analytics at scale

As the importance of data protection and privacy legislation is increasingly recognized worldwide, protecting sensitive information and individual privacy presents a major challenge for modern big data analytics systems. The ever-growing list of major data breaches (and associated fines) clearly demonstrates the inadequacy of earlier ad-hoc solutions to the problem, as well as the need to effectively bridge legal and technical/systems interpretations of data privacy. In this talk, I will present different modern privacy enhancing technologies (including federated learning, secure computing, differential privacy, and synthetic data), and discuss how they can enable formal, cryptographic notions of privacy in large-scale data analytics. The focus will be on our recent efforts to build systems and tools to support querying and machine learning over sensitive medical data. Several open challenges and directions for future research will also be discussed.