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

Abstract

AbstractData 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.