  Modern dataset search platforms employ ML task-based utility metrics instead
of relying on metadata-based keywords to comb through extensive dataset
repositories. In this setup, requesters provide an initial dataset, and the
platform identifies complementary datasets to augment (join or union) the
requester's dataset such that the ML model (e.g., linear regression)
performance is improved most. Although effective, current task-based data
searches are stymied by (1) high latency which deters users, (2) privacy
concerns for regulatory standards, and (3) low data quality which provides low
utility. We introduce Mileena, a fast, private, and high-quality task-based
dataset search platform. At its heart, Mileena is built on pre-computed
semi-ring sketches for efficient ML training and evaluation. Based on
semi-ring, we develop a novel Factorized Privacy Mechanism that makes the
search differentially private and scales to arbitrary corpus sizes and numbers
of requests without major quality degradation. We also demonstrate the early
promise in using LLM-based agents for automatic data transformation and
applying semi-rings to support causal discovery and treatment effect
estimation.
