Privacy

Van Hentenryck’s research on privacy focuses on applied differential privacy for complex applications in mobility, energy, census data, and other areas, as well as privacy-preserving federated data sharing. The  primary focus is on isolating differential privacy techniques that are accurate enough for sophisticated analytic tasks, including optimization and machine learning. Another focus is to understand the bias introduced by privacy mechanisms and their impact on decision making. In addition, there are some truly intriguing issues to investigate at the intersection of fairness and privacy.

In Press

2021

2020

2019

2018