Confidentiality without Encryption For Cloud Computational Privacy

TL;DR

This paper explores a method to ensure data confidentiality in cloud computing without encryption by adapting Rivest's chaffing and winnowing technique for cloud service models like MapReduce.

Contribution

It proposes an adaptation of the chaffing and winnowing technique to provide data confidentiality in cloud computations without encryption.

Findings

The adapted technique can be applied to MapReduce-like cloud services.

It offers a potential alternative to cryptographic methods for data privacy.

The approach is suitable for outsourcing computationally intensive tasks.

Abstract

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.