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Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis Apple Machine Learning Research

​[[{“value”:”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…”}]] [[{“value”:”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…”}]]  Read More  

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