Privately releasing conjunctions and the statistical query barrier

Proceedings of the Annual ACM Symposium on Theory of Computing

Volumn , Issue , 2011, Pages 803-812

  Gupta, Anupam ^a   Hardt, Moritz ^b   Roth, Aaron ^c   Ullman, Jonathan ^d  

^a Carnegie Mellon University   (United States)

^b Princeton University   (United States)

^c MICROSOFT RESEARCH   (United States)

^d HARVARD UNIVERSITY   (United States)

Author keywords

agnostic learning; differential privacy; submodular functions

Indexed keywords

DATA PRIVACY; ERRORS; GRAPHIC METHODS; INFORMATION ANALYSIS; LEARNING SYSTEMS; STATISTICS;

AGNOSTIC LEARNING; AVERAGE ERRORS; DIFFERENTIAL PRIVACIES; POLYNOMIAL FACTOR; PRIVACY PRESERVING; STATISTICAL QUERIES; SUBMODULAR FUNCTIONS; TRIVIAL SOLUTIONS;

LEARNING ALGORITHMS;

EID: 79959740503     PISSN: 07378017     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1993636.1993742     Document Type: Conference Paper

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