A3P: adaptive policy prediction for shared images over popular content sharing sites

  • Authors:

  • Anna Cinzia Squicciarini;Smitha Sundareswaran;Dan Lin;Josh Wede

  • Affiliations:

  • The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;Missouri Science & Technology, La Rolla, MO, USA;The Pennsylvania State University

  • Venue:
  • Proceedings of the 22nd ACM conference on Hypertext and hypermedia
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

More and more people go online today and share their personal images using popular web services like Picasa. While enjoying the convenience brought by advanced technology, people also become aware of the privacy issues of data being shared. Recent studies have highlighted that people expect more tools to allow them to regain control over their privacy. In this work, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. In particular, we examine the role of image content and metadata as possible indicators of users' privacy preferences. We propose a two-level image classification framework to obtain image categories which may be associated with similar policies. Then, we develop a policy prediction algorithm to automatically generate a policy for each newly uploaded image. Most importantly, the generated policy will follow the trend of the user's privacy concerns evolved with time. We have conducted an extensive user study and the results demonstrate effectiveness of our system with the prediction accuracy around 90%.