Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be at *12:10 pm (EST) Wednesday,
October 13*, where Ruobin Gong <https://ruobingong.github.io> (Rutgers
University) presents "Towards Good Statistical Inference from
Differentially Private Data."
*Abstract*
Differential privacy (DP) brings provability and transparency to
statistical disclosure limitation. When data users migrate their analysis
onto private data products, there is no guarantee that a statistical model,
otherwise suitable for non-private data, can still produce trustworthy
conclusions. This talk contemplates two challenges in drawing good
statistical inference from private data. When the DP mechanism is
transparent, I discuss how approximate computation techniques can be
adapted to produce exact inference with respect to the joint specification
of the intended model and the DP mechanism. In the presence of mandated
invariants which the data curator must observe, I underscore the importance
to recognize the associated privacy leakage, and advocate for the congenial
design of the DP mechanism as an alternative to optimization-based
post-processing, as a way to preserve the statistical intelligibility of
the private data product.
*Where:* CGIS Knafel Building, Room K354
(See this link <https://map.harvard.edu/?bld=04471&level=9> for directions).
*When:* Wednesday, October 13 at 12:10 - 1:30 pm.
(Bagged lunches available for pick-up at CGIS K354 *11:30 - 11:45 am*, for
the participants who responded to our previous survey. The CGIS cafe on the
first floor has been designated as an eating area, and participants may
also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm
for the presentations.)
*Zoom link*:
https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09
(For the participants who cannot join the session physically.)
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all on Wednesday!
Best,
Sooahn