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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Systematic literature review maps 13 privacy engineering dimensions into two recurrent cores mediated by modeling, with concentrations at specific lifecycle stages and domain variations.
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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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Privacy Engineering: A Systematic Literature Review
Systematic literature review maps 13 privacy engineering dimensions into two recurrent cores mediated by modeling, with concentrations at specific lifecycle stages and domain variations.