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Data integrity. Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle. [ 1] It is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the ...
Data corruption. Data corruption refers to errors in computer data that occur during writing, reading, storage, transmission, or processing, which introduce unintended changes to the original data. Computer, transmission, and storage systems use a number of measures to provide end-to-end data integrity, or lack of errors.
Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model . Normalization entails organizing the columns ...
The output of a cryptographic hash function, also known as a message digest, can provide strong assurances about data integrity, whether changes of the data are accidental (e.g., due to transmission errors) or maliciously introduced. Any modification to the data will likely be detected through a mismatching hash value.
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, [ 2] and business.
Information security, sometimes shortened to infosec, [ 1] is the practice of protecting information by mitigating information risks. It is part of information risk management. [ 2][ 3] It typically involves preventing or reducing the probability of unauthorized or inappropriate access to data or the unlawful use, disclosure, disruption ...
Referential integrity is a property of data stating that all its references are valid. In the context of relational databases, it requires that if a value of one attribute (column) of a relation (table) references a value of another attribute (either in the same or a different relation), then the referenced value must exist. [ 1]
Clark and Wilson argue that the existing integrity models such as Biba (read-up/write-down) were better suited to enforcing data integrity rather than information confidentiality. The Biba models are more clearly useful in, for example, banking classification systems to prevent the untrusted modification of information and the tainting of ...