AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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 r...
Metadata Augmented Private Language Evolution (MAPLE) extracts DP tabular metadata and uses in-context learning to firmly ground the initial synthetic distribution in the target domain, and yields a strictly better privacy-utility trade-off.
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