Fair privacy: how college students perceive fair privacy protection in online datasets

ABSTRACT

With the wide use of social media and other online services, people are getting more concerned about online privacy. Social media platforms and other online companies are collecting users’ information for various purposes, including targeted advertising. While these data are anonymous, it is possible to identify people through publicly available information and machine learning algorithms. Members of some groups are more vulnerable to such privacy attacks and more likely to be identified. This raises a concern regarding the fair or equitable protection of online privacy, or the protection of all online users’ instead of most users’ private information. This research addresses this relatively new topic from the sociological perspective and focuses on fair privacy protection in online datasets. Questionnaire data show that college students rate the current privacy protection in online datasets low, but they have great support for general privacy protection and greater support for fair privacy protection. Factors that affect their support for general and fair privacy protection include prior cautious online behavior and how essential they rate company practice and government policies that ensure fair privacy. When they perceive a lack of fair privacy in online datasets, most of them would reduce or stop using certain online services. Factors affecting such reactions include prior cautious online behavior, hours on social media, the perception of being included in online datasets, and perceived importance of fair privacy policies. The findings highlight the pivotal role of institutional privacy measures, namely fair privacy company practice and government policies, especially the latter.

KEYWORDS:

• Fair privacy
• privacy protection
• online privacy
• institutional privacy
• online datasets

ACKNOWLEDGEMENTS

This material is based upon work supported by the National Science Foundation under grant no. 2029038 (‘Privacy for All: Ensuring Fair Privacy Protection in Machine Learning.’)

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by NSF: [Grant Number 2029038].

Notes on contributors

Yu Tao

Dr. Yu Tao is an Associate Professor of Sociology at Stevens Institute of Technology in New Jersey, U.S.A. Her research analyzes issues related to human resources in science, technology, engineering, and mathematics (STEM) as well as online privacy and fair privacy from the sociological perspective. She received her B.A. in English from East China Normal University, Ed.M. in Educational Media and Technology from Boston University, and M.S. and Ph.D. in Sociology of Science and Technology from Georgia Institute of Technology.

Wendy Hui Wang

Dr. Wendy Hui Wang is an associate professor in the Computer Science Department, Stevens Institute of Technology, New Jersey. She received her PhD degree in computer science from University of British Columbia, Vancouver, Canada. Her research interests include big data security, privacy and fairness in machine learning. She is a member of the editorial boards of Journal of Information Technology and Computers & Security.