Fast and Private Max-Sum Diversification
Researchers present the first differentially private algorithms for the max-sum diversification (MSD) problem under both cardinality and matroid constraints. Their methods achieve nearly optimal utility and are faster than existing non-private approaches. Experiments on real-world datasets show that the private algorithms maintain utility comparable to non-private baselines, even under strong privacy guarantees, and offer significant speed improvements for cardinality constraints.
Why it matters: This work enables privacy-preserving data summarization and query output diversification with strong utility and efficiency, addressing a key gap in handling sensitive data.
Full story at: arXiv Cryptography and Security ↗