Degree-Based Network Anonymization

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dc.contributor.author Halawi, Ola
dc.date.accessioned 2022-04-06T08:31:33Z
dc.date.available 2022-04-06T08:31:33Z
dc.date.copyright 2020 en_US
dc.date.issued 2020-05-18
dc.identifier.uri http://hdl.handle.net/10725/13457
dc.description.abstract Enormous amounts of data collected from social networks or other online platforms are being published publicly for the sake of statistics, marketing, and research, among other objectives. The consequent privacy and data security concerns have motivated the work on degree-based data anonymization. In this thesis, we study a new multi- objective parameterized anonymization approach that generalizes the known degree anonymization problem and attempts at improving it as a more realistic model for data security/privacy. Our model suggests a convenient privacy level for each net- work based on the standard deviation of its degrees. The objective is to modify the degrees in a way that respects some given local restrictions, per node, such that the total modifications at the global level (in the whole graph/network) are bounded by some given value. A corresponding graph realization approach is introduced based on a reduction to the Weighted Edge Cover problem, which in turn is solved using Integer Linear Programming to obtain the best possible solution. Our thorough experimental studies provide empirical evidence of the effectiveness of the new approach; by specifically showing that the introduced anonymization algorithm has a negligible effect on the way nodes are clustered, thereby preserving valuable network information while significantly improving the data privacy. en_US
dc.language.iso en en_US
dc.subject Data protection en_US
dc.subject Computer networks -- Security measures en_US
dc.subject Privacy, Right of en_US
dc.subject Linear programming -- Data processing en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Degree-Based Network Anonymization en_US
dc.type Thesis en_US
dc.title.subtitle a Multi-Objective Approach en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201605565 en_US
dc.author.commembers Hamdan, May
dc.author.commembers Harati, Ramzi
dc.author.department Computer Science And Mathematics en_US
dc.description.physdesc 1 online resource (x, 40 leaves) ; ill. (some col.) en_US
dc.author.advisor Abu-Khzam, Faisal
dc.keywords Data Privacy en_US
dc.keywords Degree-based anonymization en_US
dc.keywords Parameterized complexity en_US
dc.keywords Graph realization en_US
dc.description.bibliographiccitations Bibliography: leaf 36-40. en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.325
dc.author.email ola.halawi@lau.edu en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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