Privacy
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Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
Novel differentially private model using gradient flows defined on an optimal transport metric. This Research Card introduces a novel, theoretically grounded method for differentially private generative modeling by leveraging a smooth mathematical process, achieving high-fidelity data generation with strong privacy guarantees and lower computational costs compared to traditional approaches. Title: Differentially Private Gradient Flow based…
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PETs in Advertising: Scenarios for Trusted Execution Environments
Introduction In our first article, we introduced and compared in a high-level manner two privacy-enhancing technologies (PETs) that are currently being investigated by major browser vendors [1,2] (e.g. Google Chrome & Mozilla Firefox) to address advertising use cases while meeting privacy guarantees. Our second article focused on detailing how some key advertising use cases such…