9 citations · 28 across the 11 of their papers we have counts for
11 papers
Metric Differential Privacy at the User-Level Via the Earth Mover's Distance
Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
Metric differential privacy (DP) provides heterogeneous privacy guarantees based on a distance between the pair of inputs. It is a widely popular notion of privacy since it capture…
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice
Andrea Gadotti, Florimond Houssiau, Meenatchi Sundaram Muthu Selva Annamalai +1
Behavioral data generated by users' devices, ranging from emoji use to pages visited, are collected at scale to improve apps and services. These data, however, contain fine-grained…
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees
Jacob Imola, Alessandro Epasto, Mohammad Mahdian +2
Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approxi…
Private estimation algorithms for stochastic block models and mixture models
Hongjie Chen, Vincent Cohen-Addad, Tommaso d'Orsi +4
We introduce general tools for designing efficient private estimation algorithms, in the high-dimensional settings, whose statistical guarantees almost match those of the best know…
Robustness of Locally Differentially Private Graph Analysis Against Poisoning
Jacob Imola, Amrita Roy Chowdhury, Kamalika Chaudhuri
Locally differentially private (LDP) graph analysis allows private analysis on a graph that is distributed across multiple users. However, such computations are vulnerable to data…
Differentially Private Triangle and 4-Cycle Counting in the Shuffle Model
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Subgraph counting is fundamental for analyzing connection patterns or clustering tendencies in graph data. Recent studies have applied LDP (Local Differential Privacy) to subgraph…
Communication-Efficient Triangle Counting under Local Differential Privacy
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Triangle counting in networks under LDP (Local Differential Privacy) is a fundamental task for analyzing connection patterns or calculating a clustering coefficient while strongly…
Privacy Amplification Via Bernoulli Sampling
Jacob Imola, Kamalika Chaudhuri
Balancing privacy and accuracy is a major challenge in designing differentially private machine learning algorithms. One way to improve this tradeoff for free is to leverage the no…
Online -means Clustering on Arbitrary Data Streams
Robi Bhattacharjee, Jacob Imola, Michal Moshkovitz +1
We consider online -means clustering where each new point is assigned to the nearest cluster center, after which the algorithm may update its centers. The loss incurred is the s…
Locally Differentially Private Analysis of Graph Statistics
Jacob Imola, Takao Murakami, Kamalika Chaudhuri
Differentially private analysis of graphs is widely used for releasing statistics from sensitive graphs while still preserving user privacy. Most existing algorithms however are in…
Capacity Bounded Differential Privacy
Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
Differential privacy, a notion of algorithmic stability, is a gold standard for measuring the additional risk an algorithm's output poses to the privacy of a single record in the d…