Privacy Preserving Data Mining Using Non-Negative Matrix Factorization and Singular Value Decomposition

Autor:	A.H.M. Sarowar Sattar, Afsana Afrin, Mahit Kumar Paul
Rok vydání:	2019
Předmět:	Privacy preserving Information sensitivity Computer science Singular value decomposition Process (computing) Data mining Adversary computer.software_genre computer Personally identifiable information Non-negative matrix factorization Matrix decomposition
Zdroj:	2019 4th International Conference on Electrical Information and Communication Technology (EICT).
DOI:	10.1109/eict48899.2019.9068846
Popis:	With the increasing technologies, people share information time to time from different places. The rapid growing improved technologies and improved data mining algorithms make it easy for adversary to disclose sensitive information. So there is always a second thought while individuals share their personal information. That's why privacy protection is an important consideration at every stage of data mining process. Privacy Preserving Data Mining (PPDM) maintains data utility and protects privacy at the same time. In this paper, real world datasets are perturbed by using combined Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) method. The utility of perturbed datasets is analyzed with respect to query accuracy. The result of query accuracy implies that the combined method needs to be improved or more efficient methods need to be introduced.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e9ef29fe8170e8020613764bb9c50ccd https://doi.org/10.1109/eict48899.2019.9068846 Zobrazit plný text záznamu