On Linear Refinement of Differential Privacy-Preserving Query Answering

Provided by: University of North Carolina at Chapel Hill (Kenan-Flagler)
Topic: Security
Format: PDF
It showed the necessity of incorporating a user's background knowledge to improve the accuracy of estimates from noisy responses of histogram queries. Various types of constraints (e.g., linear constraints, ordering constraints, and range constraints) may hold on the true (non-randomized) answers of histogram queries. So the idea was to apply the constraints over the noisy responses and find a new set of answers (called refinements) that are closest to the noisy responses and also satisfy known constraints.

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