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DFSCC: A Distributed Framework for Secure Computation and Sharing in the Cloud

Authors:
Mamadou Diallo
Christopher Graves
Michael August
Verinder Rana
Kevin Groarke

Keywords: Homomorphic Encryption, Secure Computing, Privacy, Machine Learning, Distributed Systems

Abstract:
Currently, various advanced data analytic tools based on machine learning and data mining techniques are available for performing data analysis in the cloud. However, these tools are not very secure since the data they operate on must be in plaintext, thereby leaving the data vulnerable to both insider and outsider attacks. In this paper, we take a different approach and propose the Distributed Framework for Secure Computation in the Cloud (DFSCC), a flexible framework for building secure, distributed computation and sharing systems. The framework takes advantage of Homomorphic Encryption (HE) techniques to enable data analytics to be performed directly on the encrypted data stored within the nodes of the distributed system. An advantage of distributing data analytics into the nodes of the framework is enhanced performance of HE-based computation. In addition, the framework incorporates a cryptographic key management infrastructure to enable secure data sharing. To evaluate the framework, we extended it to implement a system that analyzes link quality between software defined radios using a machine learning algorithm. Experiments performed on the system show performance improvement of the system as the number of nodes in the cluster is increased.

Pages: 27 to 33

Copyright: Copyright (c) IARIA, 2020

Publication date: February 23, 2020

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-775-7

Location: Lisbon, Portugal

Dates: from February 23, 2020 to February 27, 2020