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Adaptive, secure, and scalable distributed data outsourcing: a vision paper

The growing trend towards grid computing and cloud computing provides enormous potential for enabling dynamic, distributed and data-intensive applications such as sharing and processing of large-scale scientific data. It also creates an increasing challenge for automatically and dynamically placing the data in the globally distributed computers or data centers in order to optimally utilize resources while minimizing user-perceived latency. This challenge is further complicated by the security and privacy constraints on the data that are potential sensitive. In this paper, we present our vision of an adaptive, secure, and scalable data outsourcing framework for storing and processing massive, dynamic, and potentially sensitive data using distributed resources. We identify the main technical challenges and present some preliminary solutions. The key idea of the framework is that it combines data partitioning, encryption, and data reduction to ensure data confidentiality and privacy while minimizing the cost for data shipping and computation. We believe the framework will provide a holistic conceptual foundation for secure data outsourcing that enables dynamic, distributed, and data-intensive applications and will open up many exciting research challenges.