A learning theory approach to non-interactive database privacy

"... The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange an ..."

Abstract - Cited by 219 (16 self) - Add to MetaCart

The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. The current practice in data publishing relies mainly on policies and guidelines as to what types of data can be published, and agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions.

Smooth sensitivity and sampling in private data analysis

by Kobbi Nissim, Sofya Raskhodnikova, Adam Smith - In STOC , 2007

"... We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains. Our framework allows one to release functions f of the data ..."

Abstract - Cited by 173 (16 self) - Add to MetaCart

We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains. Our framework allows one to release functions f of the data with instance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also by the database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smooth sensitivity of f on the database x — a measure of variability of f in the neighborhood of the instance x. The new framework greatly expands the applicability of output perturbation, a technique for protecting individuals ’ privacy by adding a small amount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-based noise in the context of data privacy. Our framework raises many interesting algorithmic questions. Namely, to apply the framework one must compute or approximate the smooth sensitivity of f on x. We show how to do this efficiently for several different functions, including the median and the cost of the minimum spanning tree. We also give a generic procedure based on sampling that allows one to release f(x) accurately on many databases x. This procedure is applicable even when no efficient algorithm for approximating smooth sensitivity of f is known or when f is given as a black box. We illustrate the procedure by applying it to k-SED (k-means) clustering and learning mixtures of Gaussians.

A firm foundation for private data analysis

by Cynthia Dwork - Commun. ACM

"... In the information realm, loss of privacy is usually associated with failure to control access to information, to control the flow of information, or to control the purposes for which information is employed. Differential privacy arose in a context in which ensuring privacy is a challenge even if al ..."

Abstract - Cited by 139 (3 self) - Add to MetaCart

In the information realm, loss of privacy is usually associated with failure to control access to information, to control the flow of information, or to control the purposes for which information is employed. Differential privacy arose in a context in which ensuring privacy is a challenge even if all these control problems are solved: privacy-preserving statistical analysis of data. The problem of statistical disclosure control – revealing accurate statistics about a set of respondents while preserving the privacy of individuals – has a venerable history, with an extensive literature spanning statistics, theoretical computer science, security, databases, and cryptography (see, for example, the excellent survey [1], the discussion of related work in [2] and the Journal of Official Statistics 9 (2), dedicated to confidentiality and disclosure control). This long history

(Show Context)

Differential privacy via wavelet transforms

by Xiaokui Xiao, Guozhang Wang, Johannes Gehrke - In ICDE , 2010

"... Abstract — Privacy preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, ɛ-differential privacy provides one of the strongest privacy guarantees. Existing data publishing methods that achieve ɛ-differential privacy, however, offer litt ..."

Abstract - Cited by 100 (8 self) - Add to MetaCart

Abstract — Privacy preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, ɛ-differential privacy provides one of the strongest privacy guarantees. Existing data publishing methods that achieve ɛ-differential privacy, however, offer little data utility. In particular, if the output dataset is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures ɛ-differential privacy while providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution. I.

(Show Context)

What Can We Learn Privately?

by Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam Smith - 49TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE , 2008

"... Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or sp ..."

Abstract - Cited by 99 (9 self) - Add to MetaCart

Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals. We present several basic results that demonstrate general feasibility of private learning and relate several models previously studied separately in the contexts of privacy and standard learning.

(Show Context)

Universally Utility-Maximizing Privacy Mechanisms

by Arpita Ghosh, Tim Roughgarden, Mukund Sundararajan

"... A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Publishing fully accurate information maximizes utility while minimizing privacy, while publishing random noise accomplishes the opposite. Privacy can be rigor ..."

Abstract - Cited by 97 (2 self) - Add to MetaCart

A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Publishing fully accurate information maximizes utility while minimizing privacy, while publishing random noise accomplishes the opposite. Privacy can be rigorously quantified using the framework of differential privacy, which requires that a mechanism’s output distribution is nearly the same whether or not a given database row is included or excluded. The goal of this paper is strong and general utility guarantees, subject to differential privacy. We pursue mechanisms that guarantee near-optimal utility to every potential user, independent of its side information (modeled as a prior distribution over query results) and preferences (modeled via a loss function). Our main result is: for each fixed count query and differential privacy level, there is a geometric mechanism M ∗ — a discrete variant of the simple and well-studied Laplace mechanism — that is simultaneously expected loss-minimizing for every possible user, subject to the differential privacy constraint. This is an extremely strong utility guarantee: every potential user u, no matter what its side information and preferences, derives as much utility from M ∗ as from interacting with a differentially private mechanism Mu that is optimally tailored to u. More precisely, for every user u there is an optimal mecha-

(Show Context)

Optimizing linear counting queries under differential privacy

by Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, Andrew Mcgregor - In PODS ’10: Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems of data , 2010

"... Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. But despite much recent work, optimal strategies for answering a collection of related queries are not known. We propose the matrix mechanism, a new algorithm for answering a workl ..."

Abstract - Cited by 96 (12 self) - Add to MetaCart

Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. But despite much recent work, optimal strategies for answering a collection of related queries are not known. We propose the matrix mechanism, a new algorithm for answering a workload of predicate counting queries. Given a workload, the mechanism requests answers to a different set of queries, called a query strategy, which are answered using the standard Laplace mechanism. Noisy answers to the workload queries are then derived from the noisy answers to the strategy queries. This two stage process can result in a more complex correlated noise distribution that preserves differential privacy but increases accuracy. We provide a formal analysis of the error of query answers produced by the mechanism and investigate the problem of computing the optimal query strategy in support of a given workload. We show this problem can be formulated as a rank-constrained semidefinite program. Finally, we analyze two seemingly distinct techniques, whose similar behavior is explained by viewing them as instances of the matrix mechanism.

(Show Context)

Differential privacy and robust statistics

by Cynthia Dwork, Jing Lei - STOC'09 , 2009

"... We show by means of several examples that robust statistical estimators present an excellent starting point for differentially private estimators. Our algorithms use a new paradigm for differentially private mechanisms, which we call Propose-Test-Release (PTR), and for which we give a formal definit ..."

Abstract - Cited by 91 (2 self) - Add to MetaCart

We show by means of several examples that robust statistical estimators present an excellent starting point for differentially private estimators. Our algorithms use a new paradigm for differentially private mechanisms, which we call Propose-Test-Release (PTR), and for which we give a formal definition and general composition theorems.

(Show Context)

A learning theory approach to non-interactive database privacy (2008)

Tools