Safeguarding privacy in a data-intensive society
As we increasingly interact with apps and voice interfaces, we generate and share vast amounts of data that reveal our behaviors, choices and preferences. These data are rich in personal information for automatic inference, posing significant privacy concerns. To empower people to control their personal information, I will present novel approaches to protect selected attributes in images, audio, and motion data. First, I will introduce the use of feature representations that effectively disentangle sensitive from non-sensitive information, enabling the preservation of privacy while allowing for meaningful interactions. Second, I will discuss the creation of perturbations that selectively obfuscate sensitive attributes, preserving or even improving overall data quality while ensuring that sensitive information remains hidden from unwanted inferences.
As we increasingly interact with apps and voice interfaces, we generate and share vast amounts of data that reveal our behaviors, choices and preferences. These data are rich in personal information for automatic inference, posing significant privacy concerns. To empower people to control their personal information, I will present novel approaches to protect selected attributes in images, audio, and motion data. First, I will introduce the use of feature representations that effectively disentangle sensitive from non-sensitive information, enabling the preservation of privacy while allowing for meaningful interactions. Second, I will discuss the creation of perturbations that selectively obfuscate sensitive attributes, preserving or even improving overall data quality while ensuring that sensitive information remains hidden from unwanted inferences.
Speakers
Prof. Andrea Cavallaro is the Idiap Director and a Full Professor at EPFL. He serves as Editor-in-Chief of Signal Processing: Image Communication, as Senior Area Editor for the IEEE Transactions on Image Processing, as member of the IEEE Video Signal Processing and Communication Technical Committee and as member of the Technical Directions Board of the IEEE Signal Processing Society. He is a Turing Fellow at The Alan Turing Institute, the UK National Institute for Data Science and Artificial Intelligence, a Fellow of the UK Higher Education Academy and a Fellow of the International Association for Pattern Recognition (IAPR) for “contributions to image processing and multi-sensor scene understanding.”His research interests include machine learning for multimodal perception, computer vision, machine listening, and information privacy. He has published over 270 journal and conference papers, one monograph on Video tracking (2011, Wiley) and three edited books: Multi-camera networks (2009, Elsevier); Analysis, retrieval and delivery of multimedia content (2012, Springer); and Intelligent multimedia surveillance (2013, Springer). He has been Full Professor at Queen Mary University of London (QMUL) since 2010, where he was the founding Director of the Centre for Intelligent Sensing and the Director of Research of the School of Electronic Engineering and Computer Science. He received his PhD in Electrical Engineering from EPFL in 2002. He was a Research Fellow with British Telecommunications (BT) in 2004 and was awarded the Royal Academy of Engineering Teaching Prize in 2007; three student paper awards on target tracking and perceptually sensitive coding at IEEE ICASSP in 2005, 2007 and 2009; and the best paper award at IEEE AVSS 2009. He was selected as IEEE Signal Processing Society Distinguished Lecturer (2020-2021) and is the past Chair of the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee (2020-2021). He also served as elected member of the IEEE Multimedia Signal ProcessingTechnical Committee and chair of the Awards committee of the IEEE Signal Processing Society, Image, Video, and Multidimensional Signal Processing Technical Committee.
Join us on April 11th & 12th 2024
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