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securing social media users data an-adversarial approach -- 2

its about anonymizing and de-anonymizing(algorithms) social media(Twitter) user's data, then attacking the anonymized data to evaluate if theirs security leakage on the anonymized users data. using twitter(API) user's dataset AND Foursquare (API) for users location. coding in python.

ABSTRACT

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 [login to view URL] 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: Python, Machine Learning (ML), Естественный язык, Анализ и обработка данных, Article Writing

О клиенте:
( 0 отзыв(-а, -ов) ) Turkey, Turkey

ID проекта: #33626867

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