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373
Differentially private graphical degree sequences and synthetic graphs
 Privacy in Statistical Databases, volume 7556 of Lecture Notes in Computer Science
, 2012
"... Releasing the exact degree sequence of a graph for analysis may violate privacy. However, the degree sequence of a graph is an important summary statistic that is used in many statistical models. Hence a natural starting point is to release a private version of the degree sequence. A graphical degr ..."
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Cited by 3 (1 self)
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Releasing the exact degree sequence of a graph for analysis may violate privacy. However, the degree sequence of a graph is an important summary statistic that is used in many statistical models. Hence a natural starting point is to release a private version of the degree sequence. A graphical
Differentially Private Exponential Random Graphs
"... Abstract: We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner, a ..."
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Abstract: We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fitting and estimating a wide class of exponential random graph models (ERGMs) in a differentially private manner
A learning theory approach to noninteractive database privacy
 In Proceedings of the 40th annual ACM symposium on Theory of computing
, 2008
"... In this paper we demonstrate that, ignoring computational constraints, it is possible to release synthetic databases that are useful for accurately answering large classes of queries while preserving differential privacy. Specifically, we give a mechanism that privately releases synthetic data usefu ..."
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Cited by 220 (25 self)
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In this paper we demonstrate that, ignoring computational constraints, it is possible to release synthetic databases that are useful for accurately answering large classes of queries while preserving differential privacy. Specifically, we give a mechanism that privately releases synthetic data
Private analysis of graph structure
 In VLDB
, 2011
"... We present efficient algorithms for releasing useful statistics about graph data while providing rigorous privacy guarantees. Our algorithms work on data sets that consist of relationships between individuals, such as social ties or email communication. The algorithms satisfy edge differential priva ..."
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Cited by 21 (4 self)
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We present efficient algorithms for releasing useful statistics about graph data while providing rigorous privacy guarantees. Our algorithms work on data sets that consist of relationships between individuals, such as social ties or email communication. The algorithms satisfy edge differential
Iterative constructions and private data release
 In Proc. of the 9th Theory of Cryptography Conference (TCC
, 2012
"... In this paper we study the problem of approximately releasing the cut function of a graph while preserving differential privacy, and give new algorithms (and new analyses of existing algorithms) in both the interactive and noninteractive settings. Our algorithms in the interactive setting are achie ..."
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Cited by 41 (16 self)
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as releasing all cut queries on dense graphs). We also give a noninteractive algorithm for efficiently releasing private synthetic data for graph cuts with error O(V 1.5). Our algorithm is based on randomized response and a nonprivate implementation of the SDPbased, constantfactor approximation algorithm
A differentially private graph estimator
 IN INTERNATIONAL WORKSHOP ON PRIVACY ASPECTS OF DATA MINING
, 2009
"... We consider the problem of making graph databases such as social network structures available to researchers for knowledge discovery while providing privacy to the participating entities. We show that for a specific parametric graph model, the Kronecker graph model, one can construct an estimator o ..."
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Cited by 7 (1 self)
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of the true parameter in a way that both satisfies the rigorous requirements of differential privacy and is asymptotically efficient in the statistical sense. The estimator, which may then be published, defines a probability distribution on graphs. Sampling such a distribution yields a synthetic graph
A workflow for differentiallyprivate graph synthesis
, 2012
"... We present a new workflow for differentiallyprivate publication of graph topologies. First, we produce differentiallyprivate measurements of interesting graph statistics using our new version of the PINQ programming language, Weighted PINQ, which is based on a generalization of differential privacy ..."
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Cited by 3 (1 self)
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We present a new workflow for differentiallyprivate publication of graph topologies. First, we produce differentiallyprivate measurements of interesting graph statistics using our new version of the PINQ programming language, Weighted PINQ, which is based on a generalization of differential
On the complexity of differentially private data release: efficient algorithms and hardness results
 In STOC
, 2009
"... ..."
Accurate Estimation of the Degree Distribution of Private Networks
"... Abstract—We describe an efficient algorithm for releasing a provably private estimate of the degree distribution of a network. The algorithm satisfies a rigorous property of differential privacy, and is also extremely efficient, running on networks of 100 million nodes in a few seconds. Theoretical ..."
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Cited by 48 (6 self)
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Abstract—We describe an efficient algorithm for releasing a provably private estimate of the degree distribution of a network. The algorithm satisfies a rigorous property of differential privacy, and is also extremely efficient, running on networks of 100 million nodes in a few seconds. Theoretical
Private and continual release of statistics
 In Automata, Languages and Programming, volume 6199 of LNCS
, 2010
"... We ask the question: how can Web sites and data aggregators continually release updated statistics, and meanwhile preserve each individual user’s privacy? Suppose we are given a stream of 0’s and 1’s. We propose a differentially private continual counter that outputs at every time step the approxima ..."
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Cited by 47 (4 self)
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We ask the question: how can Web sites and data aggregators continually release updated statistics, and meanwhile preserve each individual user’s privacy? Suppose we are given a stream of 0’s and 1’s. We propose a differentially private continual counter that outputs at every time step
Results 1  10
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373