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What is Adversarial Learning

Handbook of Research on Cyber Crime and Information Privacy
A research field that combines machine learning and computer security.
Published in Chapter:
An Overview on Protecting User Private-Attribute Information on Social Networks
Walaa Alnasser (Arizona State University, USA), Ghazaleh Beigi (Arizona State University, USA), and Huan Liu (Arizona State University, USA)
Copyright: © 2021 |Pages: 16
DOI: 10.4018/978-1-7998-5728-0.ch006
Online social networks enable users to participate in different activities, such as connecting with each other and sharing different contents online. These activities lead to the generation of vast amounts of user data online. Publishing user-generated data causes the problem of user privacy as this data includes information about users' private and sensitive attributes. This privacy issue mandates social media data publishers to protect users' privacy by anonymizing user-generated social media data. Existing private-attribute inference attacks can be classified into two classes: friend-based private-attribute attacks and behavior-based private-attribute attacks. Consequently, various privacy protection models are proposed to protect users against private-attribute inference attacks such as k-anonymity and differential privacy. This chapter will overview and compare recent state-of-the-art researches in terms of private-attribute inference attacks and corresponding anonymization techniques. In addition, open problems and future research directions will be discussed.
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The Value Proposition of Machine Learning in Construction Management: Exploring the Trends in Construction 4.0 and Beyond
the field of machine learning which aims to study vulnerabilities of machine learning models and algorithms, and make them secure against adversarial manipulation.
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Deep Learning for Sentiment Analysis: An Overview and Perspectives
A learning paradigm based on two models attempting to achieve opposing goals.
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