3 papers
cs.DS2025
Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
Rachel Cummings, Alessandro Epasto, Jieming Mao +3
The turnstile continual release model of differential privacy captures scenarios where a privacy-preserving real-time analysis is sought for a dataset evolving through additions an…
cs.DS2023
Differentially Private -Heavy Hitters in the Sliding Window Model
Jeremiah Blocki, Seunghoon Lee, Tamalika Mukherjee +1
The data management of large companies often prioritize more recent data, as a source of higher accuracy prediction than outdated data. For example, the Facebook data policy retain…
cs.LG2021
Differentially-Private Sublinear-Time Clustering
Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee
Clustering is an essential primitive in unsupervised machine learning. We bring forth the problem of sublinear-time differentially-private clustering as a natural and well-motivate…