Marika Swanberg
I am a machine learning engineer (and researcher) on Google's Privacy Sandbox team in NYC. I graduated with my PhD from Boston University, advised by Adam Smith.
I am generally interested in real-world deployments of differential privacy (DP). In particular, I enjoy thinking about privacy risks against attackers with varying capabilities and designing accurate and scalable DP algorithms. Previously, I've dabbled in cryptography, theory of DP, and their connections to legal questions.
Research Areas
Authored Publications
Sort By
Preview abstract
Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. With the rise of foundation models, a number of new synthetic data algorithms privately finetune the weights of foundation models to improve over existing approaches to generating private synthetic data. In this work, we propose two algorithms for using API access only to generate DP tabular synthetic data. We extend the Private Evolution algorithm \citep{lin2023differentially, xie2024differentially} to the tabular data domain, define a workload-based distance measure, and propose a family of algorithms that use one-shot API access to LLMs.
View details
Preview abstract
Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. With the rise of foundation models, a number of new synthetic data algorithms privately finetune the weights of foundation models to improve over existing approaches to generating private synthetic data. In this work, we propose two algorithms for using API access only to generate DP tabular synthetic data. We extend the Private Evolution algorithm \citep{lin2023differentially, xie2024differentially} to the tabular data domain, define a workload-based distance measure, and propose a family of algorithms that use one-shot API access to LLMs.
View details
