A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary spring speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Privacy-preserving Analytics and Machine Learning with Differential Privacy

With Andreas Kopp and Lucas Rosenblatt from Microsoft

The COVID -19 pandemic demonstrates the tremendous importance of data for research, cause analysis, government action, and medical progress. However, for understandable data protection considerations,

individuals and decision-makers are often very reluctant to share personal or sensitive data. To ensure sustainable progress, we need new practices that enable insights from personal data while reliably protecting individuals' privacy.

Pioneered by Microsoft Research and associates, differential privacy is the emerging gold standard for protecting data in applications like preparing and publishing statistical analyses. Differential privacy provides a mathematically measurable privacy guarantee to individuals by adding a carefully tuned amount of statistical noise to sensitive data. It promises significantly higher privacy protection levels than commonly used disclosure limitation practices like data anonymization.

Join our session to learn about:

- What differential privacy is and how it works
- Microsoft's and Harvard's OpenDP initiative and the SmartNoise system 
- Using SmartNoise to protect sensitive data against privacy attacks
- How to create differentially private synthetic data using the new SmartNoise synthesizers
- Performing analytics, machine learning including deep learning on sensitive data using differential privacy
- The trade-off between privacy guarantee and accuracy of analytical results
- How to engage in our Early Adopter program