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Abstract
User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling - which are the intellectual property of the company - to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy preserving manner.

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Citation

Please use this url to cite or link to this publication:

Chicago
Wang, Sisi, Wing-Sea Poon, Golnoosh Farnadi, Caleb Horst, Kebra Thompson, Michael Nickels, Anderson Nascimento, and Martine De Cock. 2016. “VirtualIdentity : Privacy Preserving User Profiling.” In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2016, 1434–1437. New York, NY, USA: IEEE.
APA
Wang, Sisi, Poon, W.-S., Farnadi, G., Horst, C., Thompson, K., Nickels, M., Nascimento, A., et al. (2016). VirtualIdentity : privacy preserving user profiling. Proceedings of the 2016 IEEE/ACM international conference on advances in social networks analysis and mining ASONAM 2016 (pp. 1434–1437). Presented at the 8th IEEE/ACM International conference on Advances in Social Networks Analysis and Mining (ASONAM 2016) , New York, NY, USA: IEEE.
Vancouver
1.
Wang S, Poon W-S, Farnadi G, Horst C, Thompson K, Nickels M, et al. VirtualIdentity : privacy preserving user profiling. Proceedings of the 2016 IEEE/ACM international conference on advances in social networks analysis and mining ASONAM 2016. New York, NY, USA: IEEE; 2016. p. 1434–7.
MLA
Wang, Sisi, Wing-Sea Poon, Golnoosh Farnadi, et al. “VirtualIdentity : Privacy Preserving User Profiling.” Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2016. New York, NY, USA: IEEE, 2016. 1434–1437. Print.
@inproceedings{8513740,
  abstract     = {User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling - which are the intellectual property of the company - to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy preserving manner.},
  author       = {Wang, Sisi and Poon, Wing-Sea and Farnadi, Golnoosh and Horst, Caleb and Thompson, Kebra and Nickels, Michael and Nascimento, Anderson and De Cock, Martine},
  booktitle    = {Proceedings of the 2016 IEEE/ACM international conference on advances in social networks analysis and mining ASONAM 2016},
  isbn         = {9781509028467},
  language     = {eng},
  location     = {San Francisco, CA, USA},
  pages        = {1434--1437},
  publisher    = {IEEE},
  title        = {VirtualIdentity : privacy preserving user profiling},
  url          = {http://dx.doi.org/10.1109/asonam.2016.7752438},
  year         = {2016},
}

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