Separating Local & Shuffled Differential Privacy via Histograms

Victor Balcer, Albert Cheu, Daniel Wichs, Adam D. Smith, Yael Tauman Kalai
2020 Conference on Information-Theoretic Cryptography  
Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users' hands. We present a protocol in this model that estimates histograms with error independent of the domain size. This implies an arbitrarily large gap in sample complexity between the shuffled and local models. On the other hand, we show that the models are equivalent when we impose the constraints of pure differential privacy and single-message randomizers.
doi:10.4230/lipics.itc.2020.1 dblp:conf/icits/BalcerC20 fatcat:oucwccfamzaybgivov3hh324he