NDLI: Correlated network data publication via differential privacy

Content Provider	Springer Nature Link
Author	Chen, Rui Fung, Benjamin C. M. Yu, Philip S. Desai, Bipin C.
Copyright Year	2013
Abstract	With the increasing prevalence of information networks, research on privacy-preserving network data publishing has received substantial attention recently. There are two streams of relevant research, targeting different privacy requirements. A large body of existing works focus on preventing node re-identification against adversaries with structural background knowledge, while some other studies aim to thwart edge disclosure. In general, the line of research on preventing edge disclosure is less fruitful, largely due to lack of a formal privacy model. The recent emergence of differential privacy has shown great promise for rigorous prevention of edge disclosure. Yet recent research indicates that differential privacy is vulnerable to data correlation, which hinders its application to network data that may be inherently correlated. In this paper, we show that differential privacy could be tuned to provide provable privacy guarantees even in the correlated setting by introducing an extra parameter, which measures the extent of correlation. We subsequently provide a holistic solution for non-interactive network data publication. First, we generate a private vertex labeling for a given network dataset to make the corresponding adjacency matrix form dense clusters. Next, we adaptively identify dense regions of the adjacency matrix by a data-dependent partitioning process. Finally, we reconstruct a noisy adjacency matrix by a novel use of the exponential mechanism. To our best knowledge, this is the first work providing a practical solution for publishing real-life network data via differential privacy. Extensive experiments demonstrate that our approach performs well on different types of real-life network datasets.
Starting Page	653
Ending Page	676
Page Count	24
File Format	PDF
ISSN	10668888
Journal	The VLDB Journal
Volume Number	23
Issue Number	4
e-ISSN	0949877X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-11-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Network data Differential privacy Data correlation Non-interactive publication Database Management
Content Type	Text
Resource Type	Article
Subject	Information Systems Hardware and Architecture

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