Annabelle McIver was trained as a mathematician at Cambridge and Oxford Universities. Her research uses mathematics to analyse security flaws in computer systems. Annabelle has had a number of international visiting positions and fellowships at institutions such as MIT (USA), Birmingham (UK), LRI and INRIA (France), ETH Zurich, and was a Junior Research Fellow in Mathematics at St Hilda's College, Oxford. She is a member of the Programming Methodology technical working group of the International Federation of Information Processing. She was (co-) recipient of the 2014 Best Cybersecurity Research Paper awarded by the US National Security Agency. Her research interests include program verification, quantitative information flow, computer security, privacy.
Annabelle McIver
Mcquarie University, AUS
On Privacy and Accuracy in Data Releases
Tuesday, September 1 – 11:00
In this talk we study the relationship between privacy and accuracy in the context of correlated datasets. We use a model of quantitative information flow to describe the the trade-off between privacy of individuals' data and and the utility of queries to that data by modelling the effectiveness of adversaries attempting to make inferences after a data release. We show that, where correlations exist in datasets, it is not possible to implement optimal noise-adding mechanisms that give the best possible accuracy or the best possible privacy in all situations. Finally we illustrate the trade-off between accuracy and privacy for local and oblivious differentially private mechanisms in terms of inference attacks on medium-scale datasets.
A graph game is a two-player zero-sum game in which the players move a token throughout a graph to produce an infinite path, which determines the winner or payoff of the game. In bidding games, both players have budgets, and in each turn, we hold an “auction” (bidding) to determine which player moves the token. In this talk, we consider several bidding mechanisms and study their effect on the properties of the game. Specifically, bidding games, and in particular bidding games of infinite duration, have an intriguing equivalence with random-turn games in which in each turn, the player who moves is chosen randomly. We show how minor changes in the bidding mechanism lead to unexpected differences in the equivalence with random-turn games.
Thomas A. Henzinger is president of IST Austria (Institute of Science and Technology Austria). He holds a Dipl.-Ing. degree in Computer Science from Kepler University in Linz, Austria, an M.S. degree in Computer and Information Sciences from the University of Delaware, a Ph.D. degree in Computer Science from Stanford University (1991), and a Dr.h.c. from Fourier University in Grenoble, France (2012) and from Masaryk University in Brno, Czech Republic (2015). He was Assistant Professor of Computer Science at Cornell University (1992-95), Assistant Professor (1996-97), Associate Professor (1997-98), and Professor (1998-2004) of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He was also Director at the Max-Planck Institute for Computer Science in Saarbruecken, Germany (1999) and Professor of Computer and Communication Sciences at EPFL in Lausanne, Switzerland (2004-09). His research focuses on modern systems theory, especially models, algorithms, and tools for the design and verification of reliable software, hardware, and embedded systems. His HyTech tool was the first model checker for mixed discrete-continuous systems. He is an ISI highly cited researcher, a member of Academia Europaea, a member of the German Academy of Sciences (Leopoldina), a member of the Austrian Academy of Sciences, a Fellow of the AAAS, a Fellow of the ACM, and a Fellow of the IEEE. He has received the Milner Award of the Royal Society, the EATCS Award of the European Association for Theoretical Computer Science, the Wittgenstein Award of the Austrian Science Fund, and an ERC Advanced Investigator Grant.
Evgenia Smirni
College of William and Mary Williamsburg, USA
Machine Learning Models for Reliability of Large Scale Systems
Thursday, September 3 – 16:30
As distributed systems dramatically grow in terms of scale, complexity, and usage, understanding the hidden interactions among system and workload properties becomes an exceedingly difficult task. Machine learning models for prediction of system behavior (and analysis) are increasingly popular but their effectiveness in answering what and why is not always the most favorable. In this talk I will present two reliability analysis studies from two large, distributed systems: one that looks into GPGPU error prediction at the Titan, a large scale high-performance-computing system at ORNL, and one that analyzes the failure characteristics of solid state drives at a Google data center and hard disk drives at the Backblaze data center. Both studies illustrate the difficulty of untangling complex interactions of workload characteristics that lead to failures and of identifying failure root causes from monitored symptoms. Nevertheless, this difficulty can occasionally manifest in spectacular results where failure prediction can be dramatically accurate.
Evgenia Smirni received the Diploma degree in computer science and informatics from the University of Patras, Greece, in 1987 and the Ph.D. degree in computer science from Vanderbilt University in 1995. She is the Sidney P. Chockley Professor of computer science at William and Mary, Williamsburg, VA, USA. Her research interests include queuing networks, stochastic modeling, Markov chains, resource allocation policies, Internet and multi-tiered systems, storage systems, cloud computing, workload characterization, performance prediction and reliability of distributed systems and applications, applied machine learning. She has served as the Program Co-Chair of QEST'05, ACM Sigmetrics/Performance'06, HotMetrics'10, ICPE'17, DSN'17, HPDC'19, and SRDS'19. She also served as the General Co-Chair of QEST’10 and NSMC’10. She is an ACM Distinguished Scientist and an elected member to the IFIP W.G. 7.3.