Data Confidentiality Beyond Differential Privacy
Data Confidentiality Beyond Differential Privacy
Machine learning on personal and sensitive data raises privacy concerns and creates potential for inadvertent information leakage (e.g., extraction of one’s text messages or images from generative models). However, incorporating analysis of such data in decision making can benefit individuals and society at large (e.g., in healthcare and transportation). In order to strike a balance between these two conflicting objectives, one has to ensure that data analysis with strong confidentiality guarantees is deployed and securely implemented.
This talk will discuss challenges and opportunities in achieving this goal. The speaker will first describe attacks against not only machine learning algorithms but also naive implementations of algorithms with rigorous theoretical guarantees such as differential privacy. The speaker will then discuss approaches to mitigate some of these attack vectors, including property-preserving data analysis. To this end, the speaker will give an overview of their work on ensuring confidentiality of dataset properties that goes beyond traditional record-level privacy (e.g., focusing on protection of subpopulation information as compared to that of a single person).
Note: Per the speaker's request, no recording of this talk is available.
About the speaker
Associate Professor, The University of Melbourne
Olya Ohrimenko is an Associate Professor at The University of Melbourne which she joined in 2020. Prior to that she was a Principal Researcher at Microsoft Research in Cambridge, UK, where she started as a Postdoctoral Researcher in 2014. Her research interests include privacy and integrity of machine learning algorithms, data analysis tools and cloud computing, including topics such as differential privacy, dataset confidentiality, verifiable and data-oblivious computation, trusted execution environments, side-channel attacks and mitigations. Recently Olya has worked with the Australian Bureau of Statistics, National Bank Australia and Microsoft. She has received solo and joint research grants from Facebook and Oracle and is currently a PI on an AUSMURI grant. See https://oohrimenko.github.io for more information.
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