What can we learn privately?

SIAM Journal on Computing

Volumn 40, Issue 3, 2011, Pages 793-826

  Kasiviswanathan, Shiva Prasad ^a   Lee, Omin K ^b   Nissim, Kobbi ^c   Raskhodnikova, Sofya ^d   Smith, Adam ^d  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b Columbia University ^*  (United States)

^c BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^d The Pennsylvania State University   (United States)

Author keywords

Data privacy; Differential privacy; Probabilistically approximately correct learning; Statistical query learning

Indexed keywords

CARDINALITIES; COMPUTATION TIME; COMPUTATIONAL CONSTRAINTS; COMPUTATIONAL TASK; COMPUTATIONALLY EFFICIENT; CONCEPT CLASS; DIFFERENTIAL PRIVACIES; LEARNING PROBLEM; LOCAL ALGORITHM; LOCAL LEARNING; NOISE-TOLERANT LEARNING; NON-INTERACTIVE; PARITY FUNCTIONS; POLYNOMIAL NUMBER; PROBABILISTICALLY APPROXIMATELY CORRECT LEARNING; RANDOMIZED RESPONSE; REAL LIFE DATASETS; SAMPLE COMPLEXITY; SAMPLE SIZES; SENSITIVE INFORMATIONS; STATISTICAL QUERIES; TRAINING EXAMPLE;

DATA PRIVACY; USER INTERFACES;

LEARNING ALGORITHMS;

EID: 79960379430     PISSN: 00975397     EISSN: None     Source Type: Journal    
DOI: 10.1137/090756090     Document Type: Conference Paper

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