542 citations · 1.6k across the 85 of their papers we have counts for
12 papers · 1 filter
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…
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…
Privacy Amplification by Subsampling in Time Domain
Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri
Aggregate time-series data like traffic flow and site occupancy repeatedly sample statistics from a population across time. Such data can be profoundly useful for understanding tre…
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…
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…
Trustworthy AI Inference Systems: An Industry Research View
Rosario Cammarota, Matthias Schunter, Anand Rajan +26
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems…
Successive Refinement of Privacy
Antonious M. Girgis, Deepesh Data, Kamalika Chaudhuri +2
This work examines a novel question: how much randomness is needed to achieve local differential privacy (LDP)? A motivating scenario is providing {\em multiple levels of privacy}…
Profile-Based Privacy for Locally Private Computations
Joseph Geumlek, Kamalika Chaudhuri
Differential privacy has emerged as a gold standard in privacy-preserving data analysis. A popular variant is local differential privacy, where the data holder is the trusted curat…
Differentially Private Continual Release of Graph Statistics
Shuang Song, Susan Little, Sanjay Mehta +2
Motivated by understanding the dynamics of sensitive social networks over time, we consider the problem of continual release of statistics in a network that arrives online, while p…
Privacy constraints in regularized convex optimization
Kamalika Chaudhuri, Anand D. Sarwate
This paper is withdrawn due to some errors, which are corrected in arXiv:0912.0071v4 [cs.LG].