
Differential Privacy for the 2020 U.S. Census: Can We Make Data Both Private and Useful? (Part 1)
No se pudo agregar al carrito
Solo puedes tener X títulos en el carrito para realizar el pago.
Add to Cart failed.
Por favor prueba de nuevo más tarde
Error al Agregar a Lista de Deseos.
Por favor prueba de nuevo más tarde
Error al eliminar de la lista de deseos.
Por favor prueba de nuevo más tarde
Error al añadir a tu biblioteca
Por favor intenta de nuevo
Error al seguir el podcast
Intenta nuevamente
Error al dejar de seguir el podcast
Intenta nuevamente
-
Narrado por:
-
De:
Acerca de esta escucha
While most Americans have heard of the U.S. Census and understand that it is designed to count every resident in the United States every 10 years, many may not realize that the Census’s role goes far beyond the allocation of seats in Congress.
For this episode, we invited the three co-editors of Harvard Data Science Review’s special issue on the U.S. Census to help us explore what the Census is, what it’s used for, and how the data it collects should remain both private and useful.
Our guests are:
- Erica Groshen, former Commissioner of Labor Statistics and Head of the U.S. Bureau of Labor Statistics
- Ruobin Gong, Assistant Professor of Statistics at Rutgers University
- Salil Vadhan, Professor of Computer Science and Applied Mathematics at Harvard University
Todavía no hay opiniones