2025-09-25 –, Voyager
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.
In a world where data-driven innovation often clashes with growing privacy demands, Privacy-Enhancing Technologies (PETs) are gaining momentum. But theory and whitepapers are one thing — practical implementation is another. In this 30-minute talk, we take you behind the scenes of real-life data science projects where PETs were tested and applied.
You'll hear honest, experience-based insights about what worked, what didn’t, and why. We’ll cover widely known approaches like differential privacy, homomorphic encryption, federated learning, multi-party computation— and show where each one fits (or doesn’t) in real-world settings.
Whether you’re working in healthcare, government, HR, or finance, you’ll learn:
• How to match PET tools with specific use cases
• Where the trade-offs lie in utility, scalability, and compliance
• What technical and organizational conditions are needed for success
This session is fast-paced, practical, and designed for anyone balancing innovation with responsible data use — from data scientists and engineers to compliance leads and product managers.
A practical AI professional with 14+ years of expertise in Data Science, Analytics, Engineering, and AI, along with 5 years in leadership roles. Combines technical expertise with business acumen, gained from working with complex organizations such as IKEA, KLM, KPN, the City of Rotterdam, AON, and ING. Focusing on designing and integrating practical AI solutions that align with your organization’s objectives.
I am a Master student of Artificial Intelligence and Medical Informatics. My expertise ranges from data science and in-depth data analysis to creating human-centred interfaces and intuitive designs. In my work, I highly value developing transparent and explainable systems. I worked with various data types from different domains, including medical records.