Fermé

securing socilal media user data

Publishing social media 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.

Compétences : Python, Machine Learning (ML), Science des données, Langage Naturel, Rédaction de Rapport

Concernant le client :
( 0 commentaires ) Turkey, Turkey

Nº du projet : #34017569

14 freelances font une offre moyenne de 523 $ pour ce travail

saminakiran2

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8.1
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7.2
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6.8
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6.1
Koki1216

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6.2
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5.9
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5.1
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4.9
InoovativeThink

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4.0
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3.8
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3.4
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0.0