Leveraging Personalization To Facilitate Privacy

TL;DR

This paper proposes a personalized approach to privacy settings in online social networks, using machine learning to recommend privacy configurations based on user traits, demonstrated through a case study with positive user feedback.

Contribution

It introduces a novel paradigm for privacy options that leverages user traits for personalized privacy setting recommendations, validated by a practical web application and user study.

Findings

Users find personalized suggestions appropriate and private

Users intend to implement recommended privacy settings

Personalization can enhance privacy and security in online communities

Abstract

Online social networks have enabled new methods and modalities of collaboration and sharing. These advances bring privacy concerns: online social data is more accessible and persistent and simultaneously less contextualized than traditional social interactions. To allay these concerns, many web services allow users to configure their privacy settings based on a set of multiple-choice questions. We suggest a new paradigm for privacy options. Instead of suggesting the same defaults to each user, services can leverage knowledge of users' traits to recommend a machine-learned prediction of their privacy preferences for Facebook. As a case study, we build and evaluate MyPrivacy, a publicly available web application that suggests personalized privacy settings. An evaluation with 199 users shows that users find the suggestions to be appropriate and private; furthermore, they express intent to implement the recommendations made by MyPrivacy. This supports the proposal to put personalization to work in online communities to promote privacy and security.