Privacy-Preserving User Profiling With Facebook Likes
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)  , one of the oldest ML methods, used traditionally in collaborative filtering recommender systems.
2018 IEEE International Conference on Big Data (Big Data)
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.