Differentially Private Explanations for Clusters
Summary: DPClustX: a framework that produces differentially private (DP) global explanations for black‑box clusterings by extracting each cluster’s prominent attributes from sensitive data and privatized labels. Design mitigates noise‑accumulation and exploration‑budget issues to deliver accurate, informative cluster summaries under tight DP constraints, validated empirically on real datasets. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Amir Gilad
- 2. Tova Milo
- 3. Kathy Razmadze
- 4. Ron Zadicario
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