Privacy-Enhancing Technologies (PETs) promise to bridge the gap between data utility and privacy — but how do they perform in practice? In this talk, we’ll share real-world insights from our hands-on experience testing and implementing leading PET solutions across various data science use cases. We explored tools such as differential privacy libraries, homomorphic encryption frameworks, federated learning, multi-party computation, etc. Some lived up to their promise — others revealed critical limitations. You’ll walk away with a clear understanding of which PET solutions work best for which types of data and analysis, what trade-offs to expect, and how to set realistic goals when integrating PETs into your workflows. This session is ideal for data professionals and decision-makers who are navigating privacy risks while still wanting to innovate responsibly.