Lightweight Protocols for Distributed Private Quantile Estimation.
CoRR, February, 2025
Metric Differential Privacy at the User-Level.
CoRR, 2024
Metric Differential Privacy at the User-Level via the Earth-Mover's Distance.
Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024
Private Graph Statistics and Algorithms in Modern Applications
PhD thesis, 2023
Private estimation algorithms for stochastic block models and mixture models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees.
Proceedings of the International Conference on Machine Learning, 2023
Online k-means Clustering on Arbitrary Data Streams.
Proceedings of the International Conference on Algorithmic Learning Theory, 2023
Robustness of Locally Differentially Private Graph Analysis Against Poisoning.
CoRR, 2022
Differentially Private Subgraph Counting in the Shuffle Model.
CoRR, 2022
Communication-Efficient Triangle Counting under Local Differential Privacy.
Proceedings of the 31st USENIX Security Symposium, 2022
Balancing utility and scalability in metric differential privacy.
Proceedings of the Uncertainty in Artificial Intelligence, 2022
Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model.
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022
Privacy Amplification Via Bernoulli Sampling.
CoRR, 2021
No-Substitution $k$-means Clustering with Low Center Complexity and Memory.
CoRR, 2021
Locally Differentially Private Analysis of Graph Statistics.
Proceedings of the 30th USENIX Security Symposium, 2021
Capacity Bounded Differential Privacy.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019