From the 1 of 6 linked papers with an AI index.
6 papers
Fixed-Parameter Tractability of Private Synthetic Data Generation
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3
The paper investigates generating differentially private synthetic data and shows that the problem is fixed-parameter tractable when parameterized by the treewidth of the query fam…
Computational Hardness of Private Coreset
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3
We study the problem of differentially private (DP) computation of coreset for the -means objective. For a given input set of points, a coreset is another set of points such tha…
Urania: Differentially Private Insights into AI Use
Daogao Liu, Edith Cohen, Badih Ghazi +8
We introduce , a novel framework for generating insights about LLM chatbot interactions with rigorous differential privacy (DP) guarantees. The framework employs a private…
Private Hyperparameter Tuning with Ex-Post Guarantee
Badih Ghazi, Pritish Kamath, Alexander Knop +3
The conventional approach in differential privacy (DP) literature formulates the privacy-utility trade-off with a "privacy-first" perspective: for a predetermined level of privacy,…
On the Differential Privacy and Interactivity of Privacy Sandbox Reports
Badih Ghazi, Charlie Harrison, Arpana Hosabettu +8
The Privacy Sandbox initiative from Google includes APIs for enabling privacy-preserving advertising functionalities as part of the effort around limiting third-party cookies. In p…
PREM: Privately Answering Statistical Queries with Relative Error
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +4
We introduce (Private Relative Error Multiplicative weight update), a new framework for generating synthetic data that achieves a relative error guarantee for stati…