Privacy-preserving user profiling with Facebook likes

Sanchya Bhagat, Keerthanaa Saminathan, Anisha Agarwal, Rafael Dowsley, Martine De Cock, Anderson Nascimento

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

1 Citation (Scopus)


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
EditorsNaoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)9781538650356, 9781538650349
ISBN (Print)9781538650363
Publication statusPublished - 2019
Externally publishedYes
EventIEEE International Conference on Big Data (Big Data) 2018 - Seattle, United States of America
Duration: 10 Dec 201813 Dec 2018 (Proceedings) (Website)

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018


ConferenceIEEE International Conference on Big Data (Big Data) 2018
Abbreviated titleIEEE BigData 2018
Country/TerritoryUnited States of America
Internet address

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