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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database.It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [1]
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
For example, the remote wiping method can be manipulated by attackers to signal the process when it is not yet necessary. This results in incomplete data sanitization. If attackers do gain access to the storage on the device, the user risks exposing all private information that was stored.
Improper input validation [1] or unchecked user input is a type of vulnerability in computer software that may be used for security exploits. [2] This vulnerability is caused when "[t]he product does not validate or incorrectly validates input that can affect the control flow or data flow of a program." [1] Examples include: Buffer overflow
Software validation ensures that "you built the right thing" and confirms that the product, as provided, fulfills the intended use and goals of the stakeholders. This article has used the strict or narrow definition of verification. From a testing perspective: Fault – wrong or missing function in the code.
For example: What the user may consider as valid input may contain token characters or strings that have been reserved by the developer to have special meaning (such as the ampersand or quotation marks). The user may submit a malformed file as input that is handled properly in one application but is toxic to the receiving system.
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
In data sanitization, HTML sanitization is the process of examining an HTML document and producing a new HTML document that preserves only whatever tags and attributes are designated "safe" and desired. HTML sanitization can be used to protect against attacks such as cross-site scripting (XSS) by sanitizing any HTML code submitted by a user.