Confidentiality without Encryption For Cloud Computational Privacy
This addresses privacy concerns for cloud service consumers who are skeptical about outsourcing data, though it is an incremental adaptation of a known method.
The paper tackles the problem of data privacy in cloud computing by adapting an existing confidentiality technique without encryption to a MapReduce-like cloud service, enabling secure outsourcing of computational tasks like web log parsing and DNA sequencing.
Advances in technology has given rise to new computing models where any individual/organization (Cloud Service Consumers here by denoted as CSC's) can outsource their computational intensive tasks on their data to a remote Cloud Service Provider (CSP) for many advantages like lower costs, scalability etc. But such advantages come for a bigger cost "Security and Privacy of data" for this very reason many CSC's are skeptical to move towards cloud computing models. While the advances in cryptography research are promising, there are no practical solutions yet for performing any operations on encrypted data [1]. For this very reason there is strong need for finding alternative viable solutions for us to benefit from Cloud Computing. A technique to provide confidentiality without encryption was proposed in the past namely "Chaffing and Winnowing: Confidentiality without Encryption" by Ronald L. Rivest [2]. While this technique has been proposed for packet based communication system, its not adaptable in all cloud service models like Software-as-Service, Platform-as-Service or Infrastructure-as-Service [3]. In this paper we propose an adaptation of this technique in a cloud computational setup where CSC's outsource computational intensive tasks like web log parsing, DNA Sequencing etc to a MapReduce like CSP service.