Method and system for ensuring privacy protection for datasets using space partitioning techniques

Abstract

Systems and methods for generation of a synthetic dataset that simultaneously represents the original data and preserves privacy are provided. The objective of answering statistical queries in a differentially-private manner is addressed by providing data-independent and data-dependent algorithms based on space partitioning for differentially private dataset release. These algorithms are intuitive and transparent, resulting in theoretical results on the utility-privacy tradeoffs where utility is measured with respect to kernel density preservation. The data-dependent approach overcomes the curse of dimensionality and leads to a scalable algorithm.