Wednesday, December 18, 2019 - 2:00pm to 3:00pm

Location:

G882, Hewlett

Speaker:

Noah Golowich, MIT

Seminar group:

Abstract:

An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables circumventing the large errors that are necessary in the local model, while relying on much weaker trust assumptions than in the central model. We study basic counting problems in the shuffled model and establish separations between the error that can be achieved in the single-message shuffled model and in the shuffled model with multiple messages per user. For the frequency estimation problem with n users and for a domain of size B, we obtain:

- A nearly tight lower bound of Ω̃ (min(n^

- A nearly tight lower bound of Ω (log B / log log B) on the sample complexity with constant relative error in the single-message shuffled model. This improves on the lower bound of Ω(log^(1/17) B) obtained by Cheu et al. (Eurocrypt 2019).

- Protocols in the multi-message shuffled model with poly(log B,log n) bits of communication per user and polylog B error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms.

For the related selection problem, we also show a nearly tight sample complexity lower bound of Ω(B) in the single-message shuffled model. This improves on the Ω(B^(1/17)) lower bound obtained by Cheu et al. (Eurocrypt 2019), and when combined with their Õ (√B)-error multi-message algorithm, implies the first separation between single-message and multi-message protocols for this problem.

Joint work with Badih Ghazi, Ravi Kumar, Rasmus Pagh, and Ameya Velingker.