SHIP: Structured and Hierarchical Insight with Privacy
To capture this information, we introduce Structured and Hierarchical Insight with Privacy (SHIP), which automatically maps queries to attributes, builds a hierarchical hybrid taxonomy, and releases a DP synthetic table for further analysis.
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Key points
- Extracting insights from textual data while protecting user privacy is increasingly important.
- Existing methods produce private insights in diverse forms for different analytical purposes, making their utility difficult to assess and compare.
- In this work, we formulate differentially private (DP) insights as reusable representations for answering analytical queries, providing a common basis for utility assessment.
- Our experiments demonstrate SHIP's strong performance on both measures relative to existing private insight baselines, while identifying queries that remain challenging for structured representations.
Sources (1)
- [1]SHIP: Structured and Hierarchical Insight with PrivacyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 12:48 AM
To capture this information, we introduce Structured and Hierarchical Insight with Privacy (SHIP), which automatically maps queries to attributes, builds a hierarchical hybrid taxonomy, and releases a DP synthetic table for further analysis.
Extracting insights from textual data while protecting user privacy is increasingly important.
Extractive summary: sentences quoted from the sources.
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