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ℓdiversity: Privacy beyond kanonymity
 IN ICDE
, 2006
"... Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called kanonymity has gained popularity. In a kanonymized dataset, each record is indistinguishable from at least k − 1 other records with resp ..."
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Cited by 672 (13 self)
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Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called kanonymity has gained popularity. In a kanonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain “identifying ” attributes. In this paper we show using two simple attacks that a kanonymized dataset has some subtle, but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This kind of attack is a known problem [60]. Second, attackers often have background knowledge, and we show that kanonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks and we propose a novel and powerful privacy criterion called ℓdiversity that can defend against such attacks. In addition to building a formal foundation for ℓdiversity, we show in an experimental evaluation that ℓdiversity is practical and can be implemented efficiently.
Calibrating noise to sensitivity in private data analysis
 In Proceedings of the 3rd Theory of Cryptography Conference
, 2006
"... Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the socalled true answer is the result of applying f to the datab ..."
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Cited by 649 (60 self)
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Abstract. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the socalled true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f =P i g(xi), where xi denotes the ith row of the database and g maps database rows to [0, 1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over noninteractive. 1 Introduction We continue a line of research initiated in [10, 11] on privacy in statistical databases. A statistic is a quantity computed from a sample. Intuitively, if the database is a representative sample of an underlying population, the goal ofa privacypreserving statistical database is to enable the user to learn properties of the population as a whole while protecting the privacy of the individualcontributors.
Differential privacy: A survey of results
 In Theory and Applications of Models of Computation
, 2008
"... Abstract. Over the past five years a new approach to privacypreserving ..."
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Cited by 258 (0 self)
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Abstract. Over the past five years a new approach to privacypreserving
Practical privacy: the sulq framework
 In PODS ’05: Proceedings of the twentyfourth ACM SIGMODSIGACTSIGART symposium on Principles of database systems
, 2005
"... We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping ..."
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Cited by 223 (35 self)
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We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is P i∈S f(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise – much less than the sampling error – provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (SubLinear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large. We extend this work in two ways. First, we modify the privacy analysis to realvalued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].
Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography
, 2007
"... In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a singl ..."
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Cited by 220 (2 self)
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In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.
On the privacy preserving properties of random data perturbation techniques
 In ICDM
, 2003
"... Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacypreserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving the privacy of sensitive data ..."
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Cited by 192 (6 self)
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Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacypreserving data mining techniques. A large fraction of them use randomized data distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. This paper questions the utility of the random value distortion technique in privacy preservation. The paper notes that random objects (particularly random matrices) have “predictable ” structures in the spectral domain and it develops a random matrixbased spectral filtering technique to retrieve original data from the dataset distorted by adding random values. The paper presents the theoretical foundation of this filtering method and extensive experimental results to demonstrate that in many cases random data distortion preserve very little data privacy. 1.
Smooth sensitivity and sampling in private data analysis
 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 ..."
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Cited by 173 (16 self)
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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 instancebased 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 instancebased 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 kSED (kmeans) clustering and learning mixtures of Gaussians.
Our Data, Ourselves: Privacy via Distributed Noise Generation
 In EUROCRYPT
, 2006
"... Abstract. In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacypreserving statistical databases described in recent papers [14 ..."
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Cited by 152 (15 self)
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Abstract. In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacypreserving statistical databases described in recent papers [14,4,13]. In these databases, privacy is obtained by perturbing the true answer to a database query by the addition of a small amount of Gaussian or exponentially distributed random noise. The computational power of evenasimple form of these databases, when the queryis just of the form È i f(di), that is, the sum over all rows i in the database of a function f applied to the data in row i, has been demonstrated in [4]. A distributed implementation eliminates the need for a trusted database administrator. The results for noise generation are of independent interest. The generation of Gaussian noise introduces a technique for distributing shares of many unbiased coins with fewer executions of verifiable secret sharing than would be needed using previous approaches (reduced by afactorofn). The generation of exponentially distributed noise uses two shallow circuits: one for generating many arbitrarily but identically biased coins at an amortized cost of two unbiased random bits apiece, independent of the bias, and the other to combine bits of appropriate biases to obtain an exponential distribution. 1
Deriving private information from randomized data
 In SIGMOD
, 2005
"... Deriving private information from randomized data ..."
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Cited by 133 (2 self)
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Deriving private information from randomized data