TCS+ talk: Wednesday, November 15 — Palak Jain and Satchit Sivakumar, Boston University
The next TCS+ talk will take place this coming Wednesday, November 15th at 1:00 PM Eastern Time (10:00 AM Pacific Time, 19:00 Central European Time, 18:00 UTC). Palak Jain and Satchit Sivakumar from Boston University will speak about “The Price of Differential Privacy under Continual Observation” (abstract below).
You can reserve a spot as an individual or a group to join us live by signing up on the online form. Registration is not required to attend the interactive talk, and the link will be posted on the website the day prior to the talk; however, by registering in the form, you will receive a reminder, along with the link. (The recorded talk will also be posted on our website afterwards) As usual, for more information about the TCS+ online seminar series and the upcoming talks, or to suggest a possible topic or speaker, please see the website.
Abstract: This talk will be on the accuracy of differentially private mechanisms in the continual release model. A continual release mechanism receives a sensitive dataset as a stream of T inputs and produces, after receiving each input, an output that is accurate for all the inputs received so far.
We describe a ‘sequential embedding’ framework to prove lower bounds for the continual release model via reductions from batch model problems. (In the batch model, the algorithm receives the data as one batch and produces a single output.) We show how this framework can be used to get strong lower bounds for some fundamental problems that are widely studied; these are the first strong lower bounds on the error of continual release mechanisms and witness a polynomial in T separation between the batch model and continual release model. Previous work shows only a logarithmic in T gap between the worst-case error achievable in these two models.
Our lower bounds assume only that privacy holds for streams fixed in advance (the ‘nonadaptive’ setting). We also discuss a model that allows for adaptively selected inputs. It captures dependencies that arise in many applications of continual release. In general, both privacy and accuracy are harder to attain in this model. Nevertheless, we analyze several algorithms in the new model and, in particular, show that some of our lower bounds are matched by the error of simple algorithms whose privacy holds even for adaptively selected streams.
This talk is based on works with Iden Kalemaj, Sofya Raskhodnikova, and Adam Smith (abs/2112.00828 and abs/2306.06723).
