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A trust‐based noise injection strategy for privacy protection in cloud
Author(s) -
Zhang Gaofeng,
Yang Yun,
Yuan Dong,
Chen Jinjun
Publication year - 2012
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.1052
Subject(s) - cloud computing , obfuscation , computer security , computer science , service provider , internet privacy , service (business) , noise (video) , business , artificial intelligence , marketing , image (mathematics) , operating system
SUMMARY Cloud promises users that they can present and deploy IT services in a pay‐as‐you‐go fashion in an open and virtualized cloud environment while saving huge capital investment in their own IT infrastructure. In this sense, protection of users' privacy is critical and has become one of the most concerned issues as otherwise users may eventually lose the confidence and passion of deploying cloud in practice. Under some special cloud circumstances, some users' privacy, such as plans or habits, could be induced from their service requests by service providers without permissions from users. In this regard, obfuscation strategy can protect this kind of privacy by injecting ‘noise’ service requests to confuse potential ‘immoral’ service providers. However, existing noise obfuscation strategies focus on single noise injection whereas investigation of noise injection architecture has been neglected. Especially, a common service pattern in inter‐clouds environment, the cooperative service process including different service providers, makes the risk of privacy serious and uncontrollable by the spread of users' privacy. To address this, we present a novel trust‐based noise injection strategy for privacy protection in cloud. To support the strategy, we describe our noise injection architecture in cloud which specializes in the relations between various service roles in inter‐clouds based on our trust model. The simulation can demonstrate that our noise injection strategy could significantly improve the effectiveness of privacy protection. Copyright © 2011 John Wiley & Sons, Ltd.