Securing Social Media User Data - An Adversarial Approach

Social media users generate tremendous amounts of data. To better

serve users, it is required to share the user-related data among researchers, advertisers and application developers. Publishing such

data would raise more concerns on user privacy. To encourage data

sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help

protect privacy of social media users. In this work, we propose a

new adversarial attack specialized for social media data. We further

provide a principled way to assess effectiveness of anonymizing

different aspects of social media data. Our work sheds light on

new privacy risks in social media data due to innate heterogeneity of user-generated data which require striking balance between

sharing user data and protecting user privacy.

Taidot: Machine Learning (ML), Luonnollinen kieli, Python, Datatiede, Tietokantakehitys

Näytä lisää: data mining social media, social media data analyst, social media mothers data, social media user panel, social media marketing, data entry, how does social media collect data, social media user statistics, how to run a social media campaign for an event, explain social media marketing with an example, how does social media use data, social media big data example, social media and data analytics, what role does social media play in an seo strategy, how does social media transform data, how does social media consume data, how does social media produce data, on a social media site, data mining is referred to as quizlet, fake news detection on social media: a data mining perspective, social media marketing for an ngo, social media user in indonesia 2020

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Projektin tunnus: #32148967

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