Title
Privacy-Preserving User Profiling With Facebook Likes
Publication Date
2018
Document Type
Conference Proceeding
Abstract
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.
Publication Title
2018 IEEE International Conference on Big Data (Big Data)
First Page
5298
Last Page
5299
DOI
10.1109/bigdata.2018.8622081
Recommended Citation
Bhagat, Sanchya; Saminathan, Keerthanaa; Agarwal, Anisha; Dowsley, Rafael; Cock, Martine De; and Nascimento, Anderson C., "Privacy-Preserving User Profiling With Facebook Likes" (2018). School of Engineering and Technology Publications. 344.
https://digitalcommons.tacoma.uw.edu/tech_pub/344
