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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.

Навыки: Machine Learning (ML), Естественный язык, Python, Анализ и обработка данных, Разработка баз данных

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ID проекта: #32148967

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KolaPeters

Hi there, i have thoroughly gone through your project description, i am an experts and i can help you with it. kindly send me a message. I'm a senior engineer with rich experience in Python, Artificial Intelligence, M Больше

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freelancerIrvan

Hi there, I am a talented python dev, and I suppose I can handle this task successfully. Please let me know more details, and Please give me your chance. I look forward to hearing from you. Больше

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datacode0023

Hi, I have +5 years of experience dealing with machine learning algorithms and worked on multiple projects in this field, Please contact me to discuss more. Have a nice day

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