Diverse Community Data for Benchmarking Data Privacy Algorithms

Aniruddha Sen, Christine Task, Dhruv Kapur, Gary Stanley Howarth, Karan Bhagat

Keywords: privacy, data deidentification, synthetic data, benchmarks, data evaluation

TL;DR: The NIST Collaborative Research Cycle seeks to advance deidentification algorithms with real-world benchmark data (Diverse Community Excerpts), a suite of evaluation software (SDNist), and more than 450 deidentified instances with evaluation data.

Abstract: The Collaborative Research Cycle (CRC) is a National Institute of Standards and Technology (NIST) benchmarking program intended to strengthen understanding of tabular data deidentification technologies. Deidentification algorithms are vulnerable to the same bias and privacy issues that impact other data analytics and machine learning applications, and it can even amplify those issues by contaminating downstream applications. This paper summarizes four CRC contributions: theoretical work on the relationship between diverse populations and challenges for equitable deidentification; public benchmark data focused on diverse populations and challenging features; a comprehensive open source suite of evaluation metrology for deidentified datasets; and an archive of more than 450 deidentified data samples from a broad range of techniques. The initial set of evaluation results demonstrate the value of the CRC tools for investigations in this field.

Submission Number: 602