Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 1
Privacy preserving serial data publishing by role composition
Proceedings of the VLDB Endowment
Future Generation Computer Systems
Noise Injection for Search Privacy Protection
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Cloud computing privacy concerns on our doorstep
Communications of the ACM
International Journal of High Performance Computing Applications
A historical probability based noise generation strategy for privacy protection in cloud computing
Journal of Computer and System Sciences
A Time-Series Pattern Based Noise Generation Strategy for Privacy Protection in Cloud Computing
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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Cloud computing allows customers to utilise IT services in a pay-as-you-go fashion to save huge cost on IT infrastructure. In open cloud, 'malicious' service providers could record service data from a cloud customer and collectively deduce the customer's privacy without the customer's permission. Accordingly, customers need to take certain actions to protect their privacy automatically at client sides, such as noise obfuscation. For instance, it can generate and inject noise service requests into real ones so that service providers are hard to distinguish which ones are real. Existing noise obfuscations focus on concealing occurrence probabilities of service requests. But in reality, association probabilities of service requests can also reveal customer privacy. So, we present a novel association probability based noise generation strategy by concealing these association probabilities. The simulation comparison demonstrates that this strategy can improve the effectiveness of privacy protection significantly from the perspective of association probability.