2 papers
cs.CR2024
Distributed Differentially Private Data Analytics via Secure Sketching
Jakob Burkhardt, Hannah Keller, Claudio Orlandi +1
We introduce the linear-transformation model, a distributed model of differentially private data analysis. Clients have access to a trusted platform capable of applying a public ma…
cs.GT2024
Low-Distortion Clustering with Ordinal and Limited Cardinal Information
Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs +3
Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of agents located in an underlying metric…