The Fast and the Private: Task-based Dataset Search

TL;DR

Mileena is a novel dataset search platform that enhances speed, privacy, and quality in task-based ML dataset retrieval by leveraging semi-ring sketches and differential privacy mechanisms.

Contribution

The paper introduces Mileena, a fast and private task-based dataset search system utilizing semi-ring sketches and a new privacy mechanism, improving efficiency and privacy without sacrificing data utility.

Findings

Mileena achieves high-speed dataset search with low latency.

The system maintains differential privacy across large datasets.

Preliminary results show improved data utility and privacy balance.

Abstract

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.