Evaluating the Usability of Differential Privacy Tools with Data Practitioners

arXiv:2309.13506v2 Announce Type: replace-cross
Abstract: Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in real-world datasets and systems remains challenging. Recently developed DP tools aim to make DP implementation easier, but limited research has investigated these DP tools' usability. Through a usability study with 24 US data practitioners with varying prior DP knowledge, we evaluated the usability of four Python-based open-source DP tools: DiffPrivLib, Tumult Analytics, PipelineDP, and OpenDP. Our results suggest that …

aim analytics arxiv cs.cr cs.hc data data analytics datasets differential privacy easier implementation privacy privacy tools real research standard study systems tools usability world