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Differential Privacy

From Theory to Practice
Langbeschreibung
Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks.
Inhaltsverzeichnis
Acknowledgments.- Introduction.- A Primer on ?-Differential Privacy.- What Does DP Mean?.- Publishing Histograms for Low-dimensional Datasets.- Differentially Private Optimization.- Publishing Marginals.- The Sparse Vector Technique.- Bibliography.- Authors' Biographies.
Ninghui Li is a professor of computer science at Purdue University. His research interests are in security and privacy. He received a Bachelor's degree from the University of Science and Technology of China in 1993 and a Ph.D. in computer science from New York University in 2000. Before joining the faculty of Purdue in 2003, he was a research associate at Stanford University's Computer Science Department for three years. Prof. Li is Vice Chair of ACM Special Interest Group on Security, Audit and Control (SIGSAC). He is serving, or has served, on the editorial boards of ACM Transactions on Privacy and Security (TOPS), Journal of Computer Security (JCS), IEEE Transactions on Dependable and Secure Computing, VLDB Journal, and ACM Transactions on Internet Technology. He has served on the Program Committees of many international conferences and workshops in computer security, databases, and data mining, including serving as Program Chair for 2014 and 2015 ACM Conference on Computer and Communications Security (CCS), ACM's flagship conference in the field of security and privacy.
ISBN-13:
9783031023507
Veröffentl:
2022
Seiten:
124
Autor:
Ninghui Li
Serie:
Synthesis Lectures on Information Security, Privacy, and Trust
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch

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