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Privacy preservation of data using modified rider optimization algorithm: Optimal data sanitization and restoration model

机译：使用修改骑手优化算法的数据保留数据：最优数据消毒和恢复模型

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摘要

Data preservation is the mechanism of protecting and safeguarding the confidentiality and integrity of data. Data stored in huge databases may contain metadata, elements that may be imprecise and unstable, It may include sensitive data, personal profiles and so on, which is vulnerable to third parties such as hackers or attackers. They may misuse the data and as a consequence of this the confidentiality and privacy of the data gets lost. There is a need to conserve the data and make it available for reuse when needed. Hence, it needs a proficient method to maintain and protect individuals' data privacy regarding confidentiality and reliability. This paper intends to develop an advanced model for privacy preservation of huge data with the accomplishment of two stages, namely data sanitization and data restoration. Data sanitization process preserves the safety of sensitive data stored in huge databases, by means of hiding those sensitive data from unauthorized users. Data restoration is the process of recovering or restoring of data that is sanitized at the sender side. Concerning the secrecy, there is a need for an optimal key to hide the sensitive data at sender as well as receiver side. Subsequent to the data sanitization, it requires the same key to restore the sanitized data. Thus, the optimal key generation plays a vital role to maintain privacy preservation. In order to choose an optimal key, a modified Rider optimization Algorithm (ROA) named as Randomized ROA (RROA) model is implemented in this work. Furthermore, the efficiency of the proposed work is compared over the state-of-the-arts models by concerning the sanitization as well as restoration efficiency.
机译：数据保存是保护和维护数据的机密性和完整性的机制。存储在庞大数据库中的数据可能包含元数据，可能是不精确和不稳定的元素，它可能包括敏感数据，个人简档等，这易受黑客或攻击者等第三方。他们可能会滥用数据，因此，由于这种数据的机密性和隐私丢失。需要节省数据，并在需要时可用于重用。因此，它需要熟练的方法来维护和保护个人数据隐私，了解机密性和可靠性。本文旨在通过完成两个阶段，即数据消毒和数据恢复，开发一个先进的巨大数据的先进模型。数据消毒过程通过隐藏来自未经授权的用户的敏感数据，保留存储在庞大数据库中的敏感数据的安全性。数据恢复是恢复或恢复在发件人方面消毒的数据的过程。关于保密，需要最佳键，以隐藏发件人的敏感数据以及接收器侧。在数据消毒之后，它需要相同的键来恢复消毒数据。因此，最佳关键一代扮演维护隐私保存的重要作用。为了选择最佳键，在这项工作中实现了一种被称为随机ROA（RROA）模型的修改的骑手优化算法（ROA）。此外，通过关于消毒以及恢复效率，将拟议工作的效率与最先进的模型进行比较。

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