Jakob Burkhardt — accepted AI-conference papers

2025

Distributed Differentially Private Data Analytics via Secure Sketching

Jakob Burkhardt, Hannah Keller, Claudio Orlandi, Chris Schwiegelshohn

ICML 2025poster

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 matrix to their inputs. Such computations can be securely distributed across multiple servers using simple and efficien…

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2024

Low-Distortion Clustering with Ordinal and Limited Cardinal Information

Jakob Burkhardt, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, Sudarshan Shyam

AAAI 2024technical

Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of n agents located in an underlying metric space, our goal is to partition them into k clusters, optimizing some social cost objective. The metric space is defined…

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