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Yu Hua

Publications and source records attributed to Yu Hua.

20 records · Page 2Linked to original sources

Bandwidth-efficient Storage Services for Mitigating Side Channel Attack

Data deduplication is able to effectively identify and eliminate redundant data and only maintain a single copy of files and chunks. Hence, it is widely used in cloud storage systems to save storage space and network bandwidth. However, the occurrence of deduplication can be easily identified by monitoring and 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' privacy information by exploiting the side channel of network traffic in deduplication. Existing work addresses the LRI attack at the cost of the high bandwidth efficiency of deduplication. In order to address this problem, we propose a simple yet effective scheme, called randomized redundant chunk scheme (RRCS), to significantly mitigate the risk of the LRI attack while maintaining the high bandwidth efficiency of deduplication. The basic idea behind RRCS is to add randomized redundant chunks to mix up the real deduplication states of files used 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 of the LRI attack. We implement the RRCS prototype and evaluate it by using three large-scale real-world datasets. Experimental results demonstrate the efficiency and efficacy of RRCS.

cs.CR↗

PFO: A Parallel Friendly High Performance System for Online Query and Update of Nearest Neighbors

Nearest Neighbor(s) search is the fundamental computational primitive to tackle massive dataset. Locality Sensitive Hashing (LSH) has been a bracing tool for Nearest Neighbor(s) search in high dimensional spaces. However, traditional LSH systems cannot be applied in online big data systems to handle a large volume of query/update requests, because most of the systems optimize the query efficiency with the assumption of infrequent updates and missing the parallel-friendly design. As a result, the state-of-the-art LSH systems cannot adapt the system response to the user behavior interactively. In this paper, we propose a new LSH system called PFO. It handles query/update requests in RAM and scales the system capacity by using flash memory. To achieve high streaming data throughput, PFO adopts a parallel-friendly indexing structure while preserving the distance between data points. Further, it accommodates inbound data in real-time and dispatches update requests intelligently to eliminate the cross-threads synchronization. We carried out extensive evaluations with large synthetic and standard benchmark datasets. Results demonstrate that PFO delivers shorter latency and offers scalable capacity compared with the existing LSH systems. PFO serves with higher throughput than the state-of-the-art LSH indexing structure when dealing with online query/update requests to nearest neighbors. Meanwhile, PFO returns neighbors with much better quality, thus being efficient to handle online big data applications, e.g. streaming recommendation system, interactive machine learning systems.

cs.DC↗