23 citations · 62 across the 50 of their papers we have counts for
7 papers · 1 filter
Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries
Surendra Ghentiyala, Pritish Kamath, Ravi Kumar +1
We study the question of answering linear queries with differential privacy using few (expected) random bits. We provide a randomness-efficient analog of the -norm m…
VaultGemma: A Differentially Private Gemma Model
Amer Sinha, Thomas Mesnard, Ryan McKenna +18
We introduce VaultGemma 1B, a 1 billion parameter model within the Gemma family, fully trained with differential privacy. Pretrained on the identical data mixture used for the Gemm…
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…
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy
Yangsibo Huang, Daogao Liu, Lynn Chua +7
Machine unlearning algorithms, designed for selective removal of training data from models, have emerged as a promising approach to growing privacy concerns. In this work, we expos…
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API
Hidayet Aksu, Badih Ghazi, Pritish Kamath +4
The Privacy Sandbox Attribution Reporting API has been recently deployed by Google Chrome to support the basic advertising functionality of attribution reporting (aka conversion me…
Towards Separating Computational and Statistical Differential Privacy
Badih Ghazi, Rahul Ilango, Pritish Kamath +2
Computational differential privacy (CDP) is a natural relaxation of the standard notion of (statistical) differential privacy (SDP) proposed by Beimel, Nissim, and Omri (CRYPTO 200…