Contrastive Explainable Clustering with Differential Privacy
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909630686298112 |
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| author | Nguyen, Dung Vetzler, Ariel Kraus, Sarit Vullikanti, Anil |
| author_facet | Nguyen, Dung Vetzler, Ariel Kraus, Sarit Vullikanti, Anil |
| contents | This paper presents a novel approach to Explainable AI (XAI) that combines contrastive explanations with differential privacy for clustering algorithms. Focusing on k-median and k-means problems, we calculate contrastive explanations as the utility difference between original clustering and clustering with a centroid fixed to a specific data point. This method provides personalized insights into centroid placement. Our key contribution is demonstrating that these differentially private explanations achieve essentially the same utility bounds as non-private explanations. Experiments across various datasets show that our approach offers meaningful, privacy-preserving, and individually relevant explanations without significantly compromising clustering utility. This work advances privacy-aware machine learning by balancing data protection, explanation quality, and personalization in clustering tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04610 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contrastive Explainable Clustering with Differential Privacy Nguyen, Dung Vetzler, Ariel Kraus, Sarit Vullikanti, Anil Machine Learning Cryptography and Security This paper presents a novel approach to Explainable AI (XAI) that combines contrastive explanations with differential privacy for clustering algorithms. Focusing on k-median and k-means problems, we calculate contrastive explanations as the utility difference between original clustering and clustering with a centroid fixed to a specific data point. This method provides personalized insights into centroid placement. Our key contribution is demonstrating that these differentially private explanations achieve essentially the same utility bounds as non-private explanations. Experiments across various datasets show that our approach offers meaningful, privacy-preserving, and individually relevant explanations without significantly compromising clustering utility. This work advances privacy-aware machine learning by balancing data protection, explanation quality, and personalization in clustering tasks. |
| title | Contrastive Explainable Clustering with Differential Privacy |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2406.04610 |