PUTWorkbench: Analysing Privacy in AI-intensive Systems
This tool helps software practitioners make privacy-utility trade-off decisions in AI-intensive systems, but it is incremental as it builds on existing privacy models.
The authors tackled the challenge of balancing data utility and privacy in AI systems by developing an open-source tool called PUTWorkbench, which achieved significant results on standard and real-life datasets without requiring data science background.
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.