Version 1
: Received: 7 January 2019 / Approved: 8 January 2019 / Online: 8 January 2019 (15:40:09 CET)
How to cite:
De Luca, P. Detecting Private Information in Large Social Network using mixed Machine Learning Techniques. Preprints2019, 2019010077. https://doi.org/10.20944/preprints201901.0077.v1
De Luca, P. Detecting Private Information in Large Social Network using mixed Machine Learning Techniques. Preprints 2019, 2019010077. https://doi.org/10.20944/preprints201901.0077.v1
De Luca, P. Detecting Private Information in Large Social Network using mixed Machine Learning Techniques. Preprints2019, 2019010077. https://doi.org/10.20944/preprints201901.0077.v1
APA Style
De Luca, P. (2019). Detecting Private Information in Large Social Network using mixed Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints201901.0077.v1
Chicago/Turabian Style
De Luca, P. 2019 "Detecting Private Information in Large Social Network using mixed Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints201901.0077.v1
Abstract
The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.
Keywords
Privacy; security; Machine Learning; K-Means; Natural Language Processing; Twitter; Private Information Retrieving
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
