Designing Alternative Form-Autocompletion Tools to Enhance Privacy Decision-making and Prevent Unintended Disclosure

Author:

Knijnenburg Bart P.1ORCID,Bulgurcu Burcu2ORCID

Affiliation:

1. School of Computing, Clemson University

2. Ted Rogers School of Information Technology Management, Toronto Metropolitan University

Abstract

Modern Web browsers provide users with tools to reduce the burden of filling out forms. Despite the widespread adoption of these tools, little is known about how they affect users’ privacy decision-making. This research compares traditional form autocompletion tools with two alternative tools designed for elaboration for this study (“add” and “remove” tools). The results show that the use of traditional form autocompletion tools significantly diminishes users’ deliberate privacy decision-making, while the proposed tools can mitigate these adverse effects, such that users (1) disclose significantly less information and (2) are more likely to assess the alignment between the type of the data requested and the goal of the entity requesting that data (i.e., context specificity ). While both proposed tools help users become more deliberate in their disclosure behavior, they prefer the “add” tool over the “remove” tool. Our results show that tools designed for elaboration can nudge users toward protecting their privacy.

Publisher

Association for Computing Machinery (ACM)

Subject

Human-Computer Interaction

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