8 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…
SQUiD: Synthesizing Relational Databases from Unstructured Text
Mushtari Sadia, Zhenning Yang, Yunming Xiao +2
Relational databases are central to modern data management, yet most data exists in unstructured forms like text documents. To bridge this gap, we leverage large language models (L…
Differentially Private Quantiles with Smaller Error
Jacob Imola, Fabrizio Boninsegna, Hannah Keller +3
In the approximate quantiles problem, the goal is to output quantile estimates, the ranks of which are as close as possible to given quantiles $0 \leq q_1 \leq\dots \leq q_…
A Case for Computing on Unstructured Data
Mushtari Sadia, Amrita Roy Chowdhury, Ang Chen
Unstructured data, such as text, images, audio, and video, comprises the vast majority of the world's information, yet it remains poorly supported by traditional data systems that…
Piquant: Private Quantile Estimation in the Two-Server Model
Hannah Keller, Jacob Imola, Fabrizio Boninsegna +2
Quantiles are key in distributed analytics, but computing them over sensitive data risks privacy. Local differential privacy (LDP) offers strong protection but lower accuracy than…
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…