Abstract: Computing the frequency of items in a way that is both private and accurate is at the heart of many computational tasks. This talk presents recent results on this task in two distributed models, the local differential privacy model, and the more general multiparty differential privacy model. By combining sampling, sketching and random noise techniques, we can obtain private estimators that are unbiased, accurate, and have low communication overheads.

Bio: Graham Cormode works on topics in privacy and data summarization. He is a Fellow of the ACM, and recipient of the 2017 Adams Prize for Mathematics. He is co-author of the book "Small Summaries for Big Data".