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Differentially Private Heatmaps.pdf

2211.13454.pdf
Preview of Differentially Private Heatmaps
🔗 Source: arxiv.org
📊 Size: 11.99 MB
📄 Pages: 28 pages
⬇️ Downloads: 146

Summary

Researchers from Google propose a differentially private (DP) algorithm for generating heatmaps from aggregated user data while protecting user privacy. The algorithm's core is a DP procedure that takes a set of distributions and produces an output close to the average of the inputs, measured by Earth Mover's Distance (EMD). The researchers prove theoretical bounds on the algorithm's error under a sparsity assumption and demonstrate its advantages over previous algorithms on real-world datasets. The algorithm achieves an error of Oε(√k/n) for sparse EMD aggregation, which is tight, and also yields an improved DP algorithm for k-median clustering on the plane. Experimental results show the algorithm's practicality for moderate values of ε and a number of users equal to 200, with significant improvements over simple baselines under popular metrics for heatmaps.

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Description

Researchers from Google propose a differentially private (DP) algorithm for generating heatmaps from aggregated user data while protecting user privacy.

Technical Information

  • File Format: PDF
  • File Size: 11.99 MB
  • Pages: 28
  • Language: EN
  • Total Downloads: 146
  • Last Updated: 1 week ago

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