TY - GEN
T1 - Private PAC learning implies finite littlestone dimension
AU - Alon, Noga
AU - Livni, Roi
AU - Malliaris, Maryanthe
AU - Moran, Shay
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/6/23
Y1 - 2019/6/23
N2 - We show that every approximately differentially private learning algorithm (possibly improper) for a class H with Littlestone dimension d requires Ωlog∗(d) examples. As a corollary it follows that the class of thresholds over N can not be learned in a private manner; this resolves open questions due to [Bun et al. 2015] and [Feldman and Xiao, 2015]. We leave as an open question whether every class with a finite Littlestone dimension can be learned by an approximately differentially private algorithm.
AB - We show that every approximately differentially private learning algorithm (possibly improper) for a class H with Littlestone dimension d requires Ωlog∗(d) examples. As a corollary it follows that the class of thresholds over N can not be learned in a private manner; this resolves open questions due to [Bun et al. 2015] and [Feldman and Xiao, 2015]. We leave as an open question whether every class with a finite Littlestone dimension can be learned by an approximately differentially private algorithm.
KW - Differential Privacy
KW - Littlestone dimension
KW - PAC learning
UR - http://www.scopus.com/inward/record.url?scp=85068736790&partnerID=8YFLogxK
U2 - 10.1145/3313276.3316312
DO - 10.1145/3313276.3316312
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85068736790
T3 - Proceedings of the Annual ACM Symposium on Theory of Computing
SP - 852
EP - 860
BT - STOC 2019 - Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing
A2 - Charikar, Moses
A2 - Cohen, Edith
PB - Association for Computing Machinery
T2 - 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019
Y2 - 23 June 2019 through 26 June 2019
ER -