Distributed Private Heavy Hitters
This addresses privacy-preserving data analysis for distributed systems without a trusted administrator, offering foundational insights with broad implications for secure computation.
The paper tackles the heavy hitters problem in the fully distributed local model of differential privacy, providing tight information-theoretic bounds and computationally efficient algorithms, with results showing a separation from the centralized model.
In this paper, we give efficient algorithms and lower bounds for solving the heavy hitters problem while preserving differential privacy in the fully distributed local model. In this model, there are n parties, each of which possesses a single element from a universe of size N. The heavy hitters problem is to find the identity of the most common element shared amongst the n parties. In the local model, there is no trusted database administrator, and so the algorithm must interact with each of the $n$ parties separately, using a differentially private protocol. We give tight information-theoretic upper and lower bounds on the accuracy to which this problem can be solved in the local model (giving a separation between the local model and the more common centralized model of privacy), as well as computationally efficient algorithms even in the case where the data universe N may be exponentially large.