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Privacy Preserving Network Security Data Analytics: Architectures and System Design

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Title:Privacy Preserving Network Security Data Analytics: Architectures and System Design
Authors:DeYoung, Mark
Kobezak, Philip
Raymond, David
Marchany, Randy
Tront, Joseph
Keywords:Data Analytics Management, Governance, and Compliance
network security data analytics, privacy preserving data analytics, privacy preserving data publication
Date Issued:03 Jan 2018
Abstract:An incessant rhythm of data breaches, data leaks, and privacy exposure highlights the need to improve control over potentially sensitive data. History has shown that neither public nor private sector organizations are immune. Lax data handling, incidental leakage, and adversarial breaches are all contributing factors. Prudent organizations should consider the sensitive nature of network security data. Logged events often contain data elements that are directly correlated with sensitive information about people and their activities -- often at the same level of detail as sensor data. Our intent is to produce a database which holds network security data representative of people's interaction with the network mid-points and end-points without the problems of identifiability. In this paper we discuss architectures and propose a system design that supports a risk based approach to privacy preserving data publication of network security data that enables network security data analytics research.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/50458
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.569
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics Management, Governance, and Compliance


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