Work

To Self-Persuade or be Persuaded: Examining Interventions for Users’ Privacy Setting Selection

Year: 2022

Type: conference-paper

Abstract: User adoption of security and privacy (S&P) best practices remains low, despite sustained efforts by researchers and practitioners. Social influence is a proven method for guiding user S&P behavior, though most work has focused on studying peer influence, which is only possible with a known social graph. In a study of 104 Facebook users, we instead demonstrate that crowdsourced S&P suggestions are significantly influential. We also tested how reflective writing affected participants’ S&P decisions, with and without suggestions. With reflective writing, participants were less likely to accept suggestions — both social and Facebook default suggestions. Of particular note, when reflective writing participants were shown the Facebook default suggestion, they not only rejected it but also (unknowingly) configured their settings in accordance with expert recommendations. Our work suggests that both non-personal social influence and reflective writing can positively influence users’ S&P decisions, but have negative interactions.

Source: CHI Conference on Human Factors in Computing Systems

DOI: 10.1145/3491102.3502009

Authors: Isadora Krsek, Kimi Wenzel, Sauvik Das, Jason I. Hong, Laura Dabbish

Institutions: Carnegie Mellon University, Georgia Institute of Technology

Language: English

Topic: Privacy, Security, and Data Protection

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funders: National Science Foundation, Facebook

Awards: G3545418085, SaTC: CORE: Medium: Social Cybersecurity: Applying Social Influence to Improve Cybersecurity