Challenges and Opportunities in Security & Privacy in Machine Learning Today's talk : Kamalika Chaudhuri (UCSD) Time : 1:00pm Eastern Time Title : Beyond Differential Privacy: Two Case Studies in Private Data Analysis Abstract: Differential privacy has emerged as the gold standard in private data analysis. However, there are some use-cases where it does not directly apply. In this talk, we will look at two such use-cases and the challenges that they pose. The first is privacy of language representations, where we offer sentence-level privacy and propose a new mechanism which uses public data to maintain high fidelity. The second is privacy of location traces, where we use Gaussian process priors to model correlations in location trajectory data, and offer privacy against an inferential adversary. Joint work with Casey Meehan and Khalil Mrini Website: [ https://vsehwag.github.io/SPML_seminar/ | https://vsehwag.github.io/SPML_seminar/ ] Mailing list: [ https://groups.google.com/forum/#!forum/spml-seminars/join | Link to mailing list ] Calendar: [ https://calendar.google.com/calendar/u/0?cid=N2FwbTVxYzJsOGM2bXBiNGY4am1oMjN... | Link to calendar ] You can find all additional details on the website. If you are interested, we recommend signing up for the mailing list and sync the calendar to stay up to date with the seminar schedule.