Sharing Policies in Multiuser Privacy Scenarios

Abstract

Social network services (SNSs) enable users to conveniently share personal information. Often, the information shared concerns other people, especially other members of the SNS. In such situations, two or more people can have conflicting privacy preferences; thus, an appropriate sharing policy may not be apparent. We identify such situations asmultiuser privacy scenarios. Current approaches propose finding a sharing policy through preference aggregation. However, studies suggest that users feel more confident in their decisions regarding sharing when they know the reasons behind each other’s preferences. The goals of this paper are (1) understanding how people decide the appropriate sharing policy in multiuser scenarios where arguments are employed, and (2) developing a computational model to predict an appropriate sharing policy for a given scenario. We report on a study that involved a survey of 988 Amazon Mechanical Turk (MTurk) users about a variety of multiuser scenarios and the optimal sharing policy for each scenario. Our evaluation of the participants’ responses reveals that contextual factors, user preferences, and arguments influence the optimal sharing policy in a multiuser scenario. We develop and evaluate an inference model that predicts the optimal sharing policy given the three types of features. We analyze the predictions of our inference model to uncover potential scenario types that lead to incorrect predictions, and to enhance our understanding of when multiuser scenarios are more or less prone to dispute.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 28, 2017
Source ID
10.1145/3038920

Entities

People

  • Jose M. Such
  • Munindar P. Singh
  • Pradeep K. Murukannaiah
  • Ricard L. Fogues

Organizations

  • Engineering and Physical Sciences Research Council
  • King's College London
  • Ministry of Economy, Industry and Competitiveness
  • National Science Foundation
  • North Carolina State University
  • Rochester Institute of Technology
  • Technical University of Valencia
  • United States Department of Defense

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Naval Personnel Management
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML