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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 analysis shows that the error scales linearly with the number of unique degrees, whereas the error of conventional techniques scales linearly with the number of nodes. We complement the theoretical analysis with a thorough empirical analysis on real and synthetic graphs, showing that the algorithm’s variance and bias is low, that the error diminishes as the size of the input graph increases, and that common analyses like fitting a powerlaw can be carried out very accurately. Keywordsprivacy; social networks; privacypreserving data mining; differential privacy. I.
Coevolution of social and affiliation networks
 In 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD
, 2009
"... In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generative models which capture the statistical properties of realworld networks related only to nodetonode link formation. We ..."
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Cited by 38 (2 self)
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In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generative models which capture the statistical properties of realworld networks related only to nodetonode link formation. We propose a novel model which captures the coevolution of social and affiliation networks. We provide surprising insights into group formation based on observations in several realworld networks, showing that users often join groups for reasons other than their friends. Our experiments show that the model is able to capture both the newly observed and previously studied network properties. This work is the first to propose a generative model which captures the statistical properties of these complex networks. The proposed model facilitates controlled experiments which study the effect of actors ’ behavior on the network evolution, and it allows the generation of realistic synthetic datasets.
Sharing Graphs using Differentially Private Graph Models
"... Continuing success of research on social and computer networks requires open access to realistic measurement datasets. While these datasets can be shared, generally in the form of social or Internet graphs, doing so often risks exposing sensitive user data to the public. Unfortunately, current techn ..."
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Cited by 20 (0 self)
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Continuing success of research on social and computer networks requires open access to realistic measurement datasets. While these datasets can be shared, generally in the form of social or Internet graphs, doing so often risks exposing sensitive user data to the public. Unfortunately, current techniques to improve privacy on graphs only target specific attacks, and have been proven to be vulnerable against powerful deanonymization attacks. Our work seeks a solution to share meaningful graph datasets while preserving privacy. We observe a clear tension between strength of privacy protection and maintaining structural similarity to the original graph. To navigate the tradeoff, we develop a differentiallyprivate graph model we call Pygmalion. Given a graph G and a desired level of ǫdifferential privacy guarantee, Pygmalion extracts
kSymmetry model for identity anonymization in social networks
 In EDBT
, 2010
"... With more and more social network data being released, protecting the sensitive information within social networks from leakage has become an important concern of publishers. Adversaries with some background structural knowledge about a target individual can easily reidentify him from the network, ..."
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Cited by 11 (0 self)
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With more and more social network data being released, protecting the sensitive information within social networks from leakage has become an important concern of publishers. Adversaries with some background structural knowledge about a target individual can easily reidentify him from the network, even if the identifiers have been replaced by randomized integers(i.e., the network is naivelyanonymized). Since there exists numerous topological information that can be used to attack a victim’s privacy, to resist such structural reidentification becomes a great challenge. Previous works only investigated a minority of such structural attacks, without considering protecting against reidentification under any potential structural knowledge about a target. To achieve this objective, in this paper we propose
A Tutorial of PrivacyPreservation of Graphs and Social Networks
"... lowest bar and the States are welcome to enact more stringent rules � California State Bill 1386 � GrannLeachBliley Act of 1999 for financial institutions � COPPA for childern’s online privacy � etc. Canada ..."
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Cited by 10 (0 self)
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lowest bar and the States are welcome to enact more stringent rules � California State Bill 1386 � GrannLeachBliley Act of 1999 for financial institutions � COPPA for childern’s online privacy � etc. Canada
Injecting Uncertainty in Graphs for Identity Obfuscation
"... Data collected nowadays by socialnetworking applications create fascinating opportunities for building novel services, as well as expanding our understanding about social structures and their dynamics. Unfortunately, publishing socialnetwork graphs is considered an illadvised practice due to priva ..."
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Cited by 9 (2 self)
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Data collected nowadays by socialnetworking applications create fascinating opportunities for building novel services, as well as expanding our understanding about social structures and their dynamics. Unfortunately, publishing socialnetwork graphs is considered an illadvised practice due to privacy concerns. To alleviate this problem, several anonymization methods have been proposed, aiming at reducing the risk of a privacy breach on the published data, while still allowing to analyze them and draw relevant conclusions. In this paper we introduce a new anonymization approach that is based on injecting uncertainty in social graphs and publishingtheresultinguncertain graphs. Whileexistingapproaches obfuscate graph data by adding or removing edges entirely, we propose using a finergrained perturbation that adds or removes edges partially: this way we can achieve the same desired level of obfuscation with smaller changes in the data, thus maintaining higher utility. Our experiments on realworld networks confirm that at the same level of identity obfuscation our method provides higher usefulness than existing randomized methods that publish standard graphs. 1.
Neighborhoodprivacy protected shortest distance computing in cloud
 In SIGMOD Conference
, 2011
"... With the advent of cloud computing, it becomes desirable to utilize cloud computing to efficiently process complex operations in large graphs without compromising their sensitive information. This paper studies shortest distance computing in the cloud, which aims at the following goals: i) preventin ..."
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Cited by 9 (0 self)
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With the advent of cloud computing, it becomes desirable to utilize cloud computing to efficiently process complex operations in large graphs without compromising their sensitive information. This paper studies shortest distance computing in the cloud, which aims at the following goals: i) preventing outsourced graphs from neighborhood attack, ii) preserving shortest distances in outsourced graphs, iii) minimizing overhead on the client side. The basic idea of this paper is to transform an original graph G into a link graph Gl kept locally and a set of outsourced graphs Go. Each outsourced graph should meet the requirement of a new security model called 1neighborhooddradius. In addition, the shortest distance query can be equivalently answered using Gl and Go. Our objective is to minimize the space cost on the client side when both security and utility requirements are satisfied. We devise a greedy method to produce Gl and Go, which can exactly answer the shortest distance queries. We also develop an efficient transformation method to support approximate shortest distance answering under a given additive error bound. The final experimental results illustrate the effectiveness and efficiency of our method.
Anonymizing Weighted Social Network Graphs
"... Abstract — The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Although such analysis can facilitate better understanding of sociological, behavioral, and other interesting phenomena, there is growing concern about personal pr ..."
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Cited by 8 (1 self)
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Abstract — The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Although such analysis can facilitate better understanding of sociological, behavioral, and other interesting phenomena, there is growing concern about personal privacy being breached, thereby requiring effective anonymization techniques. In this paper, we consider edge weight anonymization in social graphs. Our approach builds a linear programming (LP) model which preserves properties of the graph that are expressible as linear functions of the edge weights. Such properties form the foundations of many important graphtheoretic algorithms such as shortest paths, knearest neighbors, minimum spanning tree, etc. Offtheshelf LP solvers can then be used to find solutions to the resulting model where the computed solution constitutes the weights in the anonymized graph. As a proof of concept, we choose the shortest paths problem, and experimentally evaluate the proposed techniques using real social network data sets. I.
Prediction Promotes Privacy In Dynamic Social Networks
"... Recent work on anonymizing online social networks (OSNs) has looked at privacy preserving techniques for publishing a single instance of the network. However, OSNs evolve and a single instance is inadequate for analyzing their evolution or performing longitudinal data analysis. We study the problem ..."
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Cited by 6 (1 self)
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Recent work on anonymizing online social networks (OSNs) has looked at privacy preserving techniques for publishing a single instance of the network. However, OSNs evolve and a single instance is inadequate for analyzing their evolution or performing longitudinal data analysis. We study the problem of repeatedly publishing OSN data as the network evolves while preserving privacy of users. Publishing multiple instances independently has privacy risks, since stitching the information together may allow an adversary to identify users. We provide methods to anonymize a dynamic network when new nodes and edges are added to the published network. These methods use link prediction algorithms to model the evolution. Using this predicted graph to perform groupbased anonymization, the loss in privacy caused by new edges can be eliminated almost entirely. We propose metrics for privacy loss, and evaluate them for publishing multiple OSN instances. 1
Reconstruction from Randomized Graph via Low Rank Approximation
"... The privacy concerns associated with data analysis over social networks have spurred recent research on privacypreserving social network analysis, particularly on privacypreserving publishing of social network data. In this paper, we focus on whether we can reconstruct a graph from the edge randomiz ..."
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Cited by 6 (1 self)
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The privacy concerns associated with data analysis over social networks have spurred recent research on privacypreserving social network analysis, particularly on privacypreserving publishing of social network data. In this paper, we focus on whether we can reconstruct a graph from the edge randomized graph such that accurate feature values can be recovered. In particular, we present a low rank approximation based reconstruction algorithm. We exploit spectral properties of the graph data and show why noise could be separated from the perturbed graph using low rank approximation. We also show key differences from previous findings of pointwise reconstruction methods on numerical data through empirical evaluations and theoretical justifications. 1