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De-anonymization
Results: 26

#	Item
21	Community-Enhanced De-anonymization of Online Social Networks Shirin Nilizadeh Apu Kapadia Add to Reading List Source URL: www.cs.indiana.edu Language: English - Date: 2014-08-26 15:36:17 Networks Network flow Community structure Connectivity Graph partition Flow network Degree distribution Graph theory Mathematics Theoretical computer science
22	Dispelling the Myths Surrounding De-identification: Anonymization Remains a Strong Tool for Protecting Privacy Ann Cavoukian, Ph.D. Add to Reading List Source URL: www.ipc.on.ca Language: English - Date: 2013-04-10 09:21:25 Privacy Data protection Law De-identification Personally identifiable information Health Insurance Portability and Accountability Act Information privacy Electronic health record Internet privacy Ethics Data privacy Privacy law
23	Dispelling the Myths Surrounding De-identification: Anonymization Remains a Strong Tool for Protecting Privacy Ann Cavoukian, Ph.D. Add to Reading List Source URL: www.futureofprivacy.org Language: English - Date: 2011-09-30 13:02:04 Law Data protection Research ethics De-identification Personally identifiable information Information privacy Health Insurance Portability and Accountability Act Internet privacy Data Protection Directive Ethics Privacy Data privacy
24	Robust De-anonymization of Large Datasets (How to Break Anonymity of the Netflix Prize Dataset) Arvind Narayanan and Vitaly Shmatikov arXiv:cs/0610105v2 [cs.CR] 22 Nov 2007 Add to Reading List Source URL: arxiv.org Language: English - Date: 2008-02-04 22:44:12 Recommender system Adversary model Netflix Data privacy Business Computer science Computing Analysis of algorithms Crowdsourcing Netflix Prize
25	Robust De-anonymization of Large Sparse Datasets Arvind Narayanan and Vitaly Shmatikov The University of Texas at Austin Abstract Add to Reading List Source URL: www.cs.utexas.edu Language: English - Date: 2008-03-08 10:27:18 Recommender system Adversary model Internet privacy Business Computing Computer science Differential privacy Crowdsourcing Netflix Prize Netflix
26	Robust De-anonymization of Large Sparse Datasets Arvind Narayanan and Vitaly Shmatikov The University of Texas at Austin Add to Reading List Source URL: www.cs.utexas.edu Language: English - Date: 2008-03-08 10:27:18 Recommender system Adversary model Internet privacy Business Computing Computer science Differential privacy Crowdsourcing Netflix Prize Netflix

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