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%. © 2011 ACM.
Recommended Citation
A. Squicciarini et al., "A3P: Adaptive Policy Prediction for Shared Images over Popular Content Sharing Sites," HT 2011 - Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, pp. 261 - 270, Association for Computing Machinery (ACM), Jan 2011.
The definitive version is available at https://doi.org/10.1145/1995966.1996000
Department(s)
Computer Science
Keywords and Phrases
Content sharing; Privacy policies
International Standard Book Number (ISBN)
978-145030256-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
01 Jan 2011
