@inproceedings{422b590e564e49c3ac2fc7d69be4709c,
title = "Toward big data risk analysis",
abstract = "The advent of social networks and Internet-of-Things has resulted in unprecedented capability of collecting, sharing and analyzing massive amounts of data. From a security perspective, Big Data may seriously weaken confidentiality, as techniques for improving Big Data analytics performance-including early fusion of heterogeneous data sources - increase the hidden redundancy of data representation, generating ill-protected copies. This gray area of redundancy triggers new disclosure threats that challenge traditional techniques to protect privacy and confidentiality. This position paper starts by proposing a definition of the Big Data Leak threat (as opposed to the one of data breach) and its role as a component of disclosure risk. Then, it discusses how a paradigm of Known, Detect, Contain and Recover could be used to establish Big Data security practices for containing disclosure risks connected to Big Data analytics.",
keywords = "Big Data, Big Data Analytics, Internet-of-things, Threat analysis",
author = "Ernesto Damiani",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 ; Conference date: 29-10-2015 Through 01-11-2015",
year = "2015",
month = dec,
day = "22",
doi = "10.1109/BigData.2015.7363966",
language = "British English",
series = "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1905--1909",
editor = "Feng Luo and Kemafor Ogan and Zaki, \{Mohammed J.\} and Laura Haas and Ooi, \{Beng Chin\} and Vipin Kumar and Sudarsan Rachuri and Saumyadipta Pyne and Howard Ho and Xiaohua Hu and Shipeng Yu and Hsiao, \{Morris Hui-I\} and Jian Li",
booktitle = "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015",
address = "United States",
}