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Title: Preserving individual privacy in context-aware ubiquitous computing environments : an intelligent and distributed agent technology for context-dependent privacy control
Author: Zhang, Ni (Jenny)
ISNI:       0000 0004 2670 5965
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2008
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Context-aware computing aims to take advantage of contextual knowledge to make decisions about how to dynamically provide services or adapt to meet user requirements. A tradeoff exists between preserving individual privacy and disclosing information to benefit from rich and interesting services. Although privacy issues have been recognized as a great barrier to the adoption and a long-term success of the context-aware computing, an extensive literature review conducted by the author has indicated that only a small subset of the privacy needs and challenges have been moderately addressed, and demand for adequate privacy protection in the context-aware paradigm is significant. This doctoral work introduces a distributed privacy protection model to tackle the challenges and overcome the limitations of existing solutions, and proposes an intelligent agent technology to facilitate a relatively unobtrusive user participation in controlling the disclosure of their sensitive information. It aims at addressing two key concerns of preserving privacy in context-aware ubiquitous computing environments: privacy feedback (i.e. notifying individuals of relevant information disclosure) and privacy management (i.e. allowing individuals to express their privacy preferences and manage their privacy levels). The proposal of the intelligent privacy agent is characterized by developing automated privacy preference mechanisms to enforce privacy control in response to context changes. More specifically, the author has developed a Privacy Policy/Preference Language to facilitate a common understanding of privacy requirements, and has exploited ontology-based methods to enable semantic policy analysis of context-dependent privacy preferences. A proof-of-concept implementation using Web Service technologies demonstrates that the proposed privacy solution can be deployed to achieve interoperability across system platforms and devices, and is scalable to the global Internet Quantatitive performance evaluations are conducted to validate the novel approaches of using hybrid reasoning mechanisms to perform the task of semantic privacy policy evaluation, preference conflict and redundancy detection, and context perception.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available