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Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world.
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Privacy-Preserving Data Mining
Agrawal, R., and Srikant, R
Cited in the paper.
Detecting Spammers on Twitter
Benevenuto, F.; Magno, G.; Rodrigues, T.; and Almeida, V
Cited in the paper.
Silentsense: Silent user identification via touch and movement behavioral biometrics
Bo, C.; Zhang, L.; Li, X.-Y.; Huang, Q.; and Wang, Y
Cited in the paper.
Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena
Bollen, J.; Mao, H.; and Pepe, A
Cited in the paper.
Privacy suites: shared privacy for social networks
Bonneau, J.; Anderson, J.; and Church, L
Cited in the paper.
Hummingbird: Privacy at the time of Twitter
De Cristofaro, E.; Soriente, C.; Tsudik, G.; and Williams, A
Cited in the paper.
A lightweight user tracking method for app providers
Frey, R. M.; Xu, R.; and Ilic, A
Cited in the paper.
Im2gps: estimating geographic information from a single image
Hays, J., and Efros, A
Cited in the paper.
How much is too much? Privacy issues on Twitter
Humphreys, L.; Gill, P.; and Krishnamurthy, B
Cited in the paper.
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N.; Li, T.; and Venkatasubramanian, S
Cited in the paper.
Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects
Malik, M. B.; Ghazi, M. A.; and Ali, R
Cited in the paper.
De-anonymizing social networks
Narayanan, A., and Shmatikov, V
Cited in the paper.
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