TY - GEN
T1 - Private learning and sanitization
T2 - 16th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2013 and the 17th International Workshop on Randomization and Computation, RANDOM 2013
AU - Beimel, Amos
AU - Nissim, Kobbi
AU - Stemmer, Uri
PY - 2013/10/15
Y1 - 2013/10/15
N2 - We compare the sample complexity of private learning and sanitization tasks under pure ε-differential privacy [Dwork, McSherry, Nissim, and Smith TCC 2006] and approximate (ε,δ)-differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy.
AB - We compare the sample complexity of private learning and sanitization tasks under pure ε-differential privacy [Dwork, McSherry, Nissim, and Smith TCC 2006] and approximate (ε,δ)-differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy.
KW - Differential Privacy
KW - Private Learning
KW - Sanitization
UR - https://www.scopus.com/pages/publications/84885226635
U2 - 10.1007/978-3-642-40328-6_26
DO - 10.1007/978-3-642-40328-6_26
M3 - Conference contribution
AN - SCOPUS:84885226635
SN - 9783642403279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 363
EP - 378
BT - Approximation, Randomization, and Combinatorial Optimization
Y2 - 21 August 2013 through 23 August 2013
ER -