Characterizing the sample complexity of private learners

DOI10.1145/2422436.2422450zbMath1362.68119OpenAlexW2096223017WikidataQ59379643 ScholiaQ59379643MaRDI QIDQ2986862

Kobbi Nissim, Amos Beimel, Uri Stemmer

Publication date: 16 May 2017

Published in: Proceedings of the 4th conference on Innovations in Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1145/2422436.2422450

zbMATH Keywords

PAC learning differential privacy probabilistic representation sample complexity

Mathematics Subject Classification ID

Computational learning theory (68Q32) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17)

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