Frequency Estimation under Local Differential Privacy [Experiments, Analysis and Benchmarks]
This work addresses the need for private data collection from large distributed populations, which is crucial for technology companies, but it is incremental as it synthesizes and benchmarks existing approaches.
The paper tackles the problem of private frequency estimation and heavy hitter identification under local differential privacy by evaluating various algorithms through extensive experiments, concluding that careful algorithm selection yields effective solutions scalable to millions of users.
Private collection of statistics from a large distributed population is an important problem, and has led to large scale deployments from several leading technology companies. The dominant approach requires each user to randomly perturb their input, leading to guarantees in the local differential privacy model. In this paper, we place the various approaches that have been suggested into a common framework, and perform an extensive series of experiments to understand the tradeoffs between different implementation choices. Our conclusion is that for the core problems of frequency estimation and heavy hitter identification, careful choice of algorithms can lead to very effective solutions that scale to millions of users