From 912383b9b8b639194b00c433474cde9d59ec0824 Mon Sep 17 00:00:00 2001 From: Chiara Braghin Date: Thu, 13 Jun 2024 16:29:02 +0200 Subject: [PATCH] nuovi DOI nella biblio --- bib_on_BigDataAccessControl.bib | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/bib_on_BigDataAccessControl.bib b/bib_on_BigDataAccessControl.bib index b2bee26..7133875 100644 --- a/bib_on_BigDataAccessControl.bib +++ b/bib_on_BigDataAccessControl.bib @@ -756,6 +756,7 @@ @article{Majeed2021AnonymizationTF year={2021}, volume={9}, pages={8512-8545}, + doi={10.1109/ACCESS.2020.3045700}, url={https://api.semanticscholar.org/CorpusID:231616865} } @@ -804,7 +805,9 @@ @book{bookMetrics isbn = {1420091484}, publisher = {Chapman \& Hall/CRC}, edition = {1st}, -abstract = {Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements. The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data. This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.} +abstract = {Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements. The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data. This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.}, +doi = {10.1201/9781420091502 }, +address="" } @InProceedings{reviewMetrics,