LAPRAS uses predictions to answer likely queries with the offline Matrix Mechanism and paces residual budget for unpredicted queries via unbiased stopping-time estimation from the first few unexpected arrivals, achieving near-offline utility when overlap is high.
Optimizing linear counting queries un- der differential privacy
2 Pith papers cite this work. Polarity classification is still indexing.
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Big Bird enforces global device-epoch individual differential privacy for multi-querier Attribution by tying privacy-loss quotas to a stock-and-flow model of impressions and conversions with per-user-action caps.
citing papers explorer
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LAPRAS : Learning-Augmented PRivate Answering for linear query Streams
LAPRAS uses predictions to answer likely queries with the offline Matrix Mechanism and paces residual budget for unpredicted queries via unbiased stopping-time estimation from the first few unexpected arrivals, achieving near-offline utility when overlap is high.
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Big Bird: Resilient Privacy Budgeting Across Untrusted Web Domains
Big Bird enforces global device-epoch individual differential privacy for multi-querier Attribution by tying privacy-loss quotas to a stock-and-flow model of impressions and conversions with per-user-action caps.