Guest Talk: Mahesh Viswanathan: Verifying the Privacy and Accuracy of Algorithms for Differential Privacy
Wednesday, January 13, 2021, 4:00pm
Location: Online Session
Speaker: Mahesh Viswanathan
Differential privacy is a mathematical framework for performing privacy-preserving computations over sensitive
data. One important feature of differential privacy algorithms is their ability to achieve provable individual privacy guarantees and at the same time ensure that the outputs are reasonably accurate. Such algorithms compute noisy versions of the right answers to aggregate queries on sensitive data to ensure privacy. Privacy guarantees demand that the algorithm running on "similar" data sets produce responses that are statistically similar; this provides a very strong form of individual privacy. Accuracy, on the other hand, demands that the algorithms output, though noised, be sufficient close to the correct answer for a query. In this talk we will present preliminary results on the algorithmic complexity of checking the privacy and accuracy requirements for a given algorithm.
Joint work with Giles Barthe, Rohit Chadha, Vishal Jagannath, Paul Krogmeier, and Prasad Sistla. Based on papers in LICS 2020 and POPL 2021.
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Meeting ID: 920 4794 9381