Abstract

Cloud computing is reserving its position in the market as the next disruptive utility paradigm. It is found on the pay-as-you-use model. Cloud computing is changing the way information technology (IT) operates for individuals as well as for companies. Cloud computing comes with different offerings to accommodate diverse applications. It comes with many successful adoption stories and a few unfortunate ones that are related to security breaches. Security concerns are what is making many companies reluctant to fully embrace the cloud realm. To enhance trust and entice adoption between cloud clients (CC) and cloud service providers (CSP), a new paradigm of depending on involving a third-party auditor (TPA) has been introduced. Hence, implementing a solution with a TPA comes with its toll in terms of trust and processing overhead. A lightweight security protocol to give the CC extra control with tools to audit the TPA and the CSP is paramount to the solution. In this paper, we are introducing a novel protocol: the lightweight accountable privacy-preserving (LAPP) protocol. Our proposed protocol is lightweight in terms of processing and communication costs. It is based on a newly introduced mathematical model along with two algorithms. We have conducted simulation experiments to measure the impact of our method. We have compared LAPP to the most eminent privacy-preserving methods in the cloud research field, using the open source cloud computing simulator GreenCloud. Our simulation results showed superiority in performance for LAPP in regard to time complexity, accuracy, and computation time on auditing. The aim of the time complexity and computation time on auditing simulations is to measure the lightweight aspect of our proposed protocol as well as to improve the quality of service.

Highlights

  • Cloud computing comes with different deployment models

  • The proposed lightweight accountable privacy-preserving (LAPP) protocol was installed on layer (C2) that consisted of 145-line cards, 234 chassis switches, and 53 ports

  • Our results show that LAPP has an accuracy of 99.98% at 27 malicious attempts, other methods are between 99.46% and 99.58%

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Summary

Introduction

Cloud computing comes with different deployment models (private, public, community, and hybrid). A TPA is introduced to assume the function of an “on-the-fly” audit This audit encompasses the security of the communication between the cloud client and, as the cloud service provider, as well as the cloud client’s data integrity [5,6]. As defined in [7], privacy preserving consists of making sure that the three cloud stakeholders are not involved in any malicious activities coming from insiders at the CSP level, as well as remediating to the TPA vulnerabilities and checking that the CC is not deceitfully affecting other clients. To preserve the CC’s privacy, we need to be able to detect whether the TPA has a dishonest role while performing the audit of the CC’s confidential information and private data.

Related Work
Proposed Solution
System Model
Key Generation
Key Update
Label Generation
Testing Process
Privacy-Preserving Polynomial Model Generation
Simulations Setup and Results
Simulation Parameters
Simulation Results
Computation Time of Auditing
Accuracy Versus the Number of Malicious Attempts
Time complexity Versus Input Files
Discussions
Conclusions
Full Text
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