@techreport{TD:100003,
	att_abstract={{(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
group-based anonymization, the loss in privacy caused
by new edges can be reduced almost entirely. We propose
metrics for privacy loss, and evaluate them for publishing
multiple OSN instances.}},
	att_authors={gc2602, ds8961, bk1836},
	att_categories={C_CCF.5, C_IIS.2, A_ST.2, A_ST.1},
	att_copyright={{USENIX}},
	att_copyright_notice={{The definitive version was published in Proceedings of WOSN 2010, Usenix. {{, 2010-06-22}}}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100003},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100003_DS1_2010-06-24T23:11:13.834Z.pdf},
	author={Graham Cormode and Divesh Srivastava and Balachander Krishnamurthy and Smriti Bhagat},
	institution={{3rd Workshop on Online Social Networks}},
	month={June},
	title={{Prediction Promotes Privacy In Dynamic Social Networks}},
	year=2010,
}