23 citations · 62 across the 50 of their papers we have counts for
14 papers · 1 filter
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…
Balls-and-Bins Sampling for DP-SGD
Lynn Chua, Badih Ghazi, Charlie Harrison +6
We introduce the Balls-and-Bins sampling for differentially private (DP) optimization methods such as DP-SGD. While it has been common practice to use some form of shuffling in DP-…
How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users
Alex Berke, Enrico Bacis, Badih Ghazi +5
Browser fingerprinting can be used to identify and track users across the Web, even without cookies, by collecting attributes from users' devices to create unique "fingerprints". T…
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
Lynn Chua, Badih Ghazi, Pritish Kamath +4
We provide new lower bounds on the privacy guarantee of the multi-epoch Adaptive Batch Linear Queries (ABLQ) mechanism with shuffled batch sampling, demonstrating substantial gaps…
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…
On Convex Optimization with Semi-Sensitive Features
Badih Ghazi, Pritish Kamath, Ravi Kumar +3
We study the differentially private (DP) empirical risk minimization (ERM) problem under the semi-sensitive DP setting where only some features are sensitive. This generalizes the…
Learning Neural Networks with Sparse Activations
Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath +1
A core component present in many successful neural network architectures, is an MLP block of two fully connected layers with a non-linear activation in between. An intriguing pheno…
On Computing Pairwise Statistics with Local Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar +2
We study the problem of computing pairwise statistics, i.e., ones of the form , where denotes the input to the th user, with…
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models
Lynn Chua, Badih Ghazi, Yangsibo Huang +6
Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, i.e…
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
Lynn Chua, Badih Ghazi, Yangsibo Huang +6
Large language models (LLMs) have emerged as powerful tools for tackling complex tasks across diverse domains, but they also raise privacy concerns when fine-tuned on sensitive dat…
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization
Badih Ghazi, Pritish Kamath, Ravi Kumar +2
In this work, we give a new technique for analyzing individualized privacy accounting via the following simple observation: if an algorithm is one-sided add-DP, then its subsampled…
Differentially Private Optimization with Sparse Gradients
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +2
Motivated by applications of large embedding models, we study differentially private (DP) optimization problems under sparsity of individual gradients. We start with new near-optim…
How Private are DP-SGD Implementations?
Lynn Chua, Badih Ghazi, Pritish Kamath +4
We demonstrate a substantial gap between the privacy guarantees of the Adaptive Batch Linear Queries (ABLQ) mechanism under different types of batch sampling: (i) Shuffling, and (i…
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features
Lynn Chua, Qiliang Cui, Badih Ghazi +9
Motivated by problems arising in digital advertising, we introduce the task of training differentially private (DP) machine learning models with semi-sensitive features. In this se…