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Teaser image to Privacy, Data Privacy, and Differential Privacy

Colloquium

Privacy, Data Privacy, and Differential Privacy

James Bailie, Harvard University

   16.07.2024

   11:00 am - 11:00 am

   LMU Department of Statistics and via zoom

This talk invites inquisitive audiences to explore the intricacies of data privacy, tracing its origins from the late 19th century to its critical importance in the digital age.

It examines Differential Privacy (DP) as a significant advancement in balancing data privacy with utility, highlighting the challenges and misconceptions that arise, particularly concerning the static view of individual data. Finally, it outlines the essential building blocks of DP and their application, using examples like the US Census, to illustrate both the theoretical and practical aspects of data privacy methodologies.

Organized by:

Department of Statistics
LMU Munich


Related

Link to Causal Inference Based on Machine Learning for Complex Longitudinal Exposures

Colloquium  •  12.11.2025  •  LMU Department of Statistics and via zoom

Causal Inference Based on Machine Learning for Complex Longitudinal Exposures

12.11.25, 4:15-5:45 pm: Iván Diaz and Herb Sussman from the New York University.


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