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Data deduplication is able to effectively identify and eliminate redundantdata and only maintain a single copy of files and chunks. Hence, it is widelyused in cloud storage systems to save storage space and network bandwidth.However, the occurrence of deduplication can be easily identified by monitoringand analyzing network traffic, which leads to the risk of user privacy leakage.The attacker can carry out a very dangerous side channel attack, i.e.,learn-the-remaining-information (LRI) attack, to reveal users' privacyinformation by exploiting the side channel of network traffic in deduplication.Existing work addresses the LRI attack at the cost of the high bandwidthefficiency of deduplication. In order to address this problem, we propose asimple yet effective scheme, called randomized redundant chunk scheme (RRCS),to significantly mitigate the risk of the LRI attack while maintaining the highbandwidth efficiency of deduplication. The basic idea behind RRCS is to addrandomized redundant chunks to mix up the real deduplication states of filesused for the LRI attack, which effectively obfuscates the view of the attacker,who attempts to exploit the side channel of network traffic for the LRI attack.Our security analysis shows that RRCS could significantly mitigate the risk ofthe LRI attack. We implement the RRCS prototype and evaluate it by using threelarge-scale real-world datasets. Experimental results demonstrate theefficiency and efficacy of RRCS.

Bandwidth-efficient Storage Services for Mitigating Side Channel Attack