A privacy-leakage-tolerance based noise enhancing strategy for privacy protection in cloud computing

Cloud computing promises a service-oriented environment where customers can utilise IT services in a pay-as-you-go fashion while saving huge capital investments on their own IT infrastructures. Due to the openness, malicious service providers may exist in these environments. Some of these service providers could record service data in cloud service processes about a customer and then collectively deduce the customer's private information without authorisation. Noise obfuscation is an effective approach in this regard by utilising noise data. For example, it can generate and inject noise service requests into real customer service requests so that service providers are not able to distinguish which ones are real ones. However, existing typical noise obfuscations do not consider the customer-defined privacy-leakage-tolerance in noise obfuscation processes. Specifically, cloud customers could define a boundary of privacy leakage possibility to require noise obfuscation on privacy protection in cloud computing. In other words, under this boundary-privacy- leakage-tolerance, noise obfuscation could be enhanced by the efficiency improvement on privacy protection, such as reducing noise service requests injected into real ones. So, the customer can obtain a lower cost on noise data in the pay-as-you-go fashion for cloud environments, with a reasonable effectiveness of privacy protection. Therefore, to address this privacy concern, a novel noise enhancing strategy can be presented. We firstly analyse the privacy-leakage-tolerance for cloud customers in terms of noise generation. Then, the creation of a noise generation set can be presented based on the privacy-leakage-tolerance, and the set can guide and enhance existing noise generation strategies by this boundary. Lastly, we present our novel privacy-leakage-tolerance based noise enhancing strategy for privacy protection in cloud computing. The simulation evaluation demonstrates that our strategy can significantly improve the efficiency of privacy protection on existing noise obfuscations in cloud environments.