How can we design a cost-effective model that ensures a high level of data integrity protection?

Alouane Nour-Eddine, Abouchabaka Jaafar, R. Najat
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Abstract

Everyone agree that data is more secured locally than when it is outsourced far away from their owners. But the growth of local data annually implies extra charges for the customers, which makes their business slowing down. Cloud computing paradigm comes with new technologies that offer a very economic and cost-effective solutions, but at the expense of security. So designing a lightweight system that can achieve a balance between cost and data security is really important. Several schemes and techniques has been proposed for securing, checking and repairing data, but unfortunately the majority doesn't respect and preserve the cost efficiency and profitability of cloud offers. In this paper we try to answer the question: how can we design a model that enables a high level of integrity check while preserving a minimum cost? We try also to analyze a new threat model regards the tracking of a file's fragments during a repair or a download operation, which can cause the total loss of customers data. The solution given in this paper is based on redistributing fragments locations after every data operation using a set of random values generated by a chaotic map. Finally, we provide a data loss insurance (data corruption as well) approach based on user estimation of data importance level that helps in reducing user concerns about data loss.
我们如何设计一个经济有效的模型来确保高水平的数据完整性保护?
每个人都同意,数据在本地比在远离所有者的地方外包更安全。但是,每年本地数据量的增长意味着客户需要支付额外费用,这使得他们的业务放缓。云计算范式伴随着新技术而来,这些技术提供了非常经济和经济的解决方案,但以牺牲安全性为代价。所以设计一个轻量级的系统,在成本和数据安全之间取得平衡是非常重要的。已经提出了几种方案和技术来保护、检查和修复数据,但不幸的是,大多数方案和技术都不尊重和保留云提供的成本效率和盈利能力。在本文中,我们试图回答这样一个问题:我们如何设计一个模型,在保持最低成本的同时实现高水平的完整性检查?我们还试图分析一种新的威胁模型,即在修复或下载操作期间跟踪文件的碎片,这可能导致客户数据的全部丢失。本文给出的解决方案是利用混沌映射生成的一组随机值,在每次数据操作后重新分配碎片位置。最后,我们提供了一种基于用户对数据重要性级别的估计的数据丢失保险(数据损坏)方法,有助于减少用户对数据丢失的担忧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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