AI intensive systems that operate upon user data face the challenge of balancing data utility with privacy concerns. We propose the idea and present the prototype of an open-source tool called Privacy Utility Trade-off (PUT) Workbench which seeks to aid software practitioners to take such crucial decisions. We pick a simple privacy model that doesn't require any background knowledge in Data Science and show how even that can achieve significant results over standard and real-life datasets. The tool and the source code is made freely available for extensions and usage.
@article{arxiv.1902.01580,
title = {PUTWorkbench: Analysing Privacy in AI-intensive Systems},
author = {Saurabh Srivastava and Vinay P. Namboodiri and T. V. Prabhakar},
journal= {arXiv preprint arXiv:1902.01580},
year = {2019}
}