Abstract
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.
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Notes
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It shares some issues with cold-start problem. Usability problem is naive user’s lack of experience to build a CBPM matrix.
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Acknowledgments
This work was supported, in part, by grants from the US National Science Foundation (under Grant Nos. IIS-1110868, CNS-0619069, EPS-0701890, OISE-0729792, CNS-0855248 and EPS-0918970) and the US Office of Naval Research (under Grant Numbers N000141010091 and N000141410489). We gratefully acknowledge this support.
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Venakata Swamy, M., Agarwal, N. & Ramaswamy, S. C-CBPM: collective context based privacy model. J Ambient Intell Human Comput 5, 881–895 (2014). https://doi.org/10.1007/s12652-014-0241-z
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DOI: https://doi.org/10.1007/s12652-014-0241-z