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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)974-989
Number of pages16
JournalInformation Communication and Society
Issue number5
StatePublished - 2023

ASJC Scopus subject areas

  • Communication
  • Library and Information Sciences


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


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