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For example, an input box accepting numeric data may reject the letter 'O'. File existence check Checks that a file with a specified name exists. This check is essential for programs that use file handling. Format check Checks that the data is in a specified format (template), e.g., dates have to be in the format YYYY-MM-DD.
For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [ 2 ] and transforming it into one cohesive data set; a simple example is the ...
The Challenge of Multilevel Security gives an example of a sanitization failure caused by unexpected behavior in Microsoft Word's change tracking feature. [ 7 ] The two most common mistakes for incorrectly redacting a document are adding an image layer over the sensitive text to obscure it, without removing the underlying text, and setting the ...
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.
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.
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
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.
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.