Researchers can now learn discrete distributions with person-level privacy guarantees at near-optimal sample complexity using polynomial-time algorithms—a significant advance over prior work that either required exponential time or suboptimal sample sizes.
This paper develops efficient algorithms for learning discrete probability distributions when data comes from multiple users, each contributing samples, while protecting individual privacy.