Application-Aware Client-Side Data Reduction and Encryption of Personal Data in Cloud Backup Services

• Published in: Journal of computer science and technology Vol. 28; no. 6; pp. 1012 - 1024
• Main Author: 付印金 肖侬 廖湘科 刘芳
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.11.2013; Springer Nature B.V; State Key Laboratory of High Performance Computing, National University of Defense Technology Changsha 410073, China%School of Computer, National University of Defense Technology, Changsha 410073, China
• Subjects: Artificial Intelligence; Back up systems; Backups; Cloud computing; Computer architecture; Computer Science; Cybersecurity; Data compression; Data encryption; Data integrity; Data reduction; Data storage; Data Structures and Information Theory; Data transfer (computers); Datasets; Information management; Information Systems Applications (incl.Internet); Personal information; Privacy; R&D; Regular Paper; Research & development; Software Engineering; Studies; Theory of Computation; Wide area networks; 个人资料; 加密; 备份窗口; 客户端; 应用; 感知; 服务; 缩减技术; data reduction; application awareness; selective encryption; cloud backup
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-013-1394-5

Cloud backup has been an important issue ever since large quantities of valuable data have been stored on the personal computing devices. Data reduction techniques, such as deduplication, delta encoding, and Lempel-Ziv （LZ） compression, performed at the client side before data transfer can help ease cloud backup by saving network bandwidth and reducing cloud storage space. However, client-side data reduction in cloud backup services faces efficiency and privacy challenges. In this paper, we present Pangolin, a secure and efficient cloud backup service for personal data storage by exploiting application awareness. It can speedup backup operations by application-aware client-side data reduction technique, and mitigate data security risks by integrating selective encryption into data reduction for sensitive applications. Our experimental evaluation, based on a prototype implementation, shows that our scheme can improve data reduction efficiency over the state-of-the-art methods by shortening the backup window size to 33%-75%, and its security mechanism for＇ sensitive applications has negligible impact on backup window size.