Ensuring Data Privacy Using Machine Learning for Responsible Data Science

Autor:	Somula Ramasubbareddy, Millena Debaprada Jena, Bhabendu Kumar Mohanta, Sunil Samanta Singhar
Rok vydání:	2020
Předmět:	Information privacy Computer science business.industry Big data Cryptography Artificial intelligence business Machine learning computer.software_genre Data science Human being computer Private information retrieval
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9789811556784 FICTA (2)
DOI:	10.1007/978-981-15-5679-1_49
Popis:	With the advancement use of computers extensively the use of data has also grown to big data level. Nowadays data is collected without any specific purpose, every activity of a machine or a human being is recorded, If needed in the future then the data will be analyzed. But here the question of trust arises as the data will go through many phases for the analysis by different parties. The data may contain some sensitive or private information which can be misutilized by the organizations involved in the analysis stages. So it is needed for the hour to consider the data privacy issues very seriously. Different types of methods have been proposed in this paper to ensure data privacy and also different machine learning algorithms have been discussed which have been used to design the proposed methods to ensure data privacy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::02f02b745c62f8e00bb2e44bc418fbe0 https://doi.org/10.1007/978-981-15-5679-1_49 Zobrazit plný text záznamu