Privacy-Preserving User Profiling with Facebook Likes

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Sanchya Bhagat, University of Washington Tacoma
  • ,
  • Keerthanaa Saminathan, University of Washington Tacoma
  • ,
  • Anisha Agarwal, University of Washington Tacoma
  • ,
  • Rafael Dowsley
  • ,
  • Martine De Cock, University of Washington Tacoma, Universiteit Gent
  • ,
  • Anderson Nascimento, University of Washington Tacoma

The content generated by users on social media is rich in personal information that can be mined to construct accurate user profiles, and subsequently used for tailored advertising or other personalized services. Facebook has recently come under scrutiny after a third party gained access to the data of millions of users and mined it to construct psychographical profiles, which were allegedly used to influence voters in elections. As part of a possible solution to avoid data breaches while still being able to perform meaningful machine learning (ML) on social media data, we propose a privacy-preserving algorithm for k-nearest neighbor (kNN) [1] , one of the oldest ML methods, used traditionally in collaborative filtering recommender systems.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
Number of pages2
Publication year2019
Article number8622081
ISBN (Electronic)9781538650356
Publication statusPublished - 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: 10 Dec 201813 Dec 2018


Conference2018 IEEE International Conference on Big Data, Big Data 2018
LandUnited States
SponsorBaidu, et al, Expedia Group, IEEE, IEEE Computer Society, Squirrel AI Learning

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