TY - GEN

T1 - Private coresets

AU - Feldman, Dan

AU - Fiat, Amos

AU - Kaplan, Haim

AU - Nissim, Kobbi

PY - 2009

Y1 - 2009

N2 - A coreset of a point set P is a small weighted set of points that captures some geometric properties of P. Coresets have found use in a vast host of geometric settings. We forge a link between coresets, and differentially private sanitizations that can answer any number of queries without compromising privacy. We define the notion of private coresets, which are simultaneously both coresets and differentially private, and show how they may be constructed. We first show that the existence of a small coreset with low generalized sensitivity (i.e., replacing a single point in the original point set slightly affects the quality of the coreset) implies (in an inefficient manner) the existence of a private coreset for the same queries. This greatly extends the works of Blum, Ligett, and Roth [STOC 2008] and McSherry and Talwar [FOCS 2007]. We also give an efficient algorithm to compute private coresets for k-median and k-mean queries in R d, immediately implying efficient differentially private sanitizations for such queries. Following McSherry and Talwar, this construction also gives efficient coalition proof (approximately dominant strategy) mechanisms for location problems. Unlike coresets which only have a multiplicative approximation factor, we prove that private coresets must exhibit additive error. We present a new technique for showing lower bounds on this error.

AB - A coreset of a point set P is a small weighted set of points that captures some geometric properties of P. Coresets have found use in a vast host of geometric settings. We forge a link between coresets, and differentially private sanitizations that can answer any number of queries without compromising privacy. We define the notion of private coresets, which are simultaneously both coresets and differentially private, and show how they may be constructed. We first show that the existence of a small coreset with low generalized sensitivity (i.e., replacing a single point in the original point set slightly affects the quality of the coreset) implies (in an inefficient manner) the existence of a private coreset for the same queries. This greatly extends the works of Blum, Ligett, and Roth [STOC 2008] and McSherry and Talwar [FOCS 2007]. We also give an efficient algorithm to compute private coresets for k-median and k-mean queries in R d, immediately implying efficient differentially private sanitizations for such queries. Following McSherry and Talwar, this construction also gives efficient coalition proof (approximately dominant strategy) mechanisms for location problems. Unlike coresets which only have a multiplicative approximation factor, we prove that private coresets must exhibit additive error. We present a new technique for showing lower bounds on this error.

KW - Coresets

KW - Differential privacy

KW - Privacy

UR - http://www.scopus.com/inward/record.url?scp=70350700899&partnerID=8YFLogxK

U2 - 10.1145/1536414.1536465

DO - 10.1145/1536414.1536465

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AN - SCOPUS:70350700899

SN - 9781605585062

T3 - Proceedings of the Annual ACM Symposium on Theory of Computing

SP - 361

EP - 370

BT - STOC'09 - Proceedings of the 2009 ACM International Symposium on Theory of Computing

T2 - 41st Annual ACM Symposium on Theory of Computing, STOC '09

Y2 - 31 May 2009 through 2 June 2009

ER -