securing social media users data

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

Skills: Article Writing, Machine Learning (ML), Natural Language, Python, Data Scraping

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