Years and Authors of Summarized Original Work
2010; Hardt, Rothblum
2012; Hardt, Rothblum, Servedio
2012; Thaler, Ullman, Vadhan
2013; Ullman
2014; Chandrasekaran, Thaler, Ullman, Wan
2014; Bun, Ullman, Vadhan
Problem Definition
Our goal is to design differentially private algorithms to answer statistical queries on a sensitive database. We model the database \(D = (x_{1},\ldots,x_{n}) \in (\{0,1\}^{d})^{n}\) as a collection of n records – one per individual – each consisting of d binary attributes. A differentially private algorithm is a randomized algorithm whose output distribution does not depend “significantly” on any one record of the database. The formal definition is as follows:
Definition 1 ([8])
An algorithm \(\mathcal{A}:(\{0,1\}^{d})^{n}\rightarrow\)\(\mathcal{R}\) is \((\varepsilon,\delta )\)-differentially private if for every pair of databases \(D,D^{{\prime}}\in (\{0,1\}^{d})^{n}\) that differ on at most one row and every \(S \subseteq \mathcal{R}\),
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Arora S, Hazan E, Kale S (2012) The multiplicative weights update method: a meta-algorithm and applications. Theory Comput 8(1):121–164
Blum A, Dwork C, McSherry F, Nissim K (2005) Practical privacy: the SuLQ framework. In: PODS. ACM, Baltimore MD, pp 128–138
Blum A, Ligett K, Roth A (2013) A learning theory approach to noninteractive database privacy. J ACM 60(2):12
Bun M, Ullman J, Vadhan SP (2014) Fingerprinting codes and the price of approximate differential privacy. In: STOC. ACM, New York, NY, pp 1–10
Chandrasekaran K, Thaler J, Ullman J, Wan A (2014) Faster private release of marginals on small databases. In: ITCS. ACM, Princeton, NJ, pp 387–402
Dinur I, Nissim K (2003) Revealing information while preserving privacy. In: PODS. ACM, San Diego, CA, pp 202–210
Dwork C, Nissim K (2004) Privacy-preserving datamining on vertically partitioned databases. In: CRYPTO, Santa Barbara, CA, pp 528–544
Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Halevi S, Rabin T (eds) TCC. Lecture notes in computer science, vol 3876. Springer, New York, NY, pp 265–284
Gupta A, Hardt M, Roth A, Ullman J (2013) Privately releasing conjunctions and the statistical query barrier. SIAM J Comput 42(4):1494–1520
Hardt M, Rothblum G (2010) A multiplicative weights mechanism for privacy-preserving data analysis. In: Proceedings of the 51st foundations of computer science (FOCS). IEEE, Las Vegas, NV, pp 61–70
Hardt M, Rothblum GN, Servedio RA (2012) Private data release via learning thresholds. In: SODA. SIAM, Kyoto, Japan, pp 168–187
Thaler J, Ullman J, Vadhan SP (2012) Faster algorithms for privately releasing marginals. In: ICALP (1). Springer, Warwick, UK, pp 810–821
Ullman J (2013) Answering n2+o(1) counting queries with differential privacy is hard. In: STOC. ACM, Palo Alto, CA, pp 361–370
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this entry
Cite this entry
Ullman, J. (2016). Query Release via Online Learning. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_551
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2864-4_551
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-2863-7
Online ISBN: 978-1-4939-2864-4
eBook Packages: Computer ScienceReference Module Computer Science and Engineering