Fingerprinting codes are a crucial tool for proving lower bounds in differential privacy. They have been used to prove tight lower bounds for several fundamental questions, especially in the “low accuracy” regime. Unlike reconstruction/discrepancy approaches however, they are more suited for proving worst-case lower bounds, for query sets that arise naturally from the fingerprinting codes construction. In this work, we propose a general framework for proving fingerprinting type lower bounds, that allows us to tailor the technique to the geometry of the query set.
Our approach allows us to…Apple Machine Learning Research