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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TurtleKV uses a balanced TurtleTree on-disk structure and flexible memory tuning knobs to deliver strong performance across inserts, mixed workloads, point queries, and scans in YCSB tests, matching or beating SplinterDB, RocksDB, and WiredTiger.
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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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Dynamic read & write optimization with TurtleKV
TurtleKV uses a balanced TurtleTree on-disk structure and flexible memory tuning knobs to deliver strong performance across inserts, mixed workloads, point queries, and scans in YCSB tests, matching or beating SplinterDB, RocksDB, and WiredTiger.