C-CBPM: collective context based privacy model

Existing research on developing privacy models, although seem persuasive, are essentially based on user, role or service identification. Such models are incapable of automatically adjusting privacy needs of consumers or organizations to the context in which the data is accessed. In this work we pres...

Full description

Saved in:
Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2014-12, Vol.5 (6), p.881-895
Main Authors: Venakata Swamy, M., Agarwal, Nitin, Ramaswamy, Srini
Format: Article
Language:English
Subjects:
Access control
Artificial Intelligence
Collaboration
Computational Intelligence
Context
Crowdsourcing
Disaster relief
Donations
Earthquakes
Engineering
Food
Humanitarianism
Intelligence
Machine learning
Original Research
Participation
Privacy
Robotics and Automation
Social networks
Usability
User generated content
User Interfaces and Human Computer Interaction
Virtual communities
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Existing research on developing privacy models, although seem persuasive, are essentially based on user, role or service identification. Such models are incapable of automatically adjusting privacy needs of consumers or organizations to the context in which the data is accessed. In this work we present a context based privacy model (CBPM) that leverages the context in which the information content is accessed. We introduce the concepts of donation and adoption for privacy configuration by extending CBPM to collective-CBPM (C-CBPM), bearing the analysis upon the notions of collective intelligence and trust computation. The efficacy of the proposed C-CBPM model is demonstrated by implementing it in various application domains such as, humanitarian assistance and disaster relief operations. CBPM is also implemented as a framework that assists in the comparison and extension of existing role or service based privacy and access control models.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-014-0241-z