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Data sanitization information


Data sanitization involves the secure and permanent erasure of sensitive data from datasets and media to guarantee that no residual data can be recovered even through extensive forensic analysis.[1] Data sanitization has a wide range of applications but is mainly used for clearing out end-of-life electronic devices or for the sharing and use of large datasets that contain sensitive information. The main strategies for erasing personal data from devices are physical destruction, cryptographic erasure, and data erasure. While the term data sanitization may lead some to believe that it only includes data on electronic media, the term also broadly covers physical media, such as paper copies. These data types are termed soft for electronic files and hard for physical media paper copies. Data sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity.[2]

This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to another individual. The importance of data sanitization has risen in recent years as private information is increasingly stored in an electronic format and larger, more complex datasets are being utilized to distribute private information. Electronic storage has expanded and enabled more private data to be stored. Therefore it requires more advanced and thorough data sanitization techniques to ensure that no data is left on the device once it is no longer in use. Technological tools that enable the transfer of large amounts of data also allow more private data to be shared. Especially with the increasing popularity of cloud-based information sharing and storage, data sanitization methods that ensure that all data shared is cleaned has become a significant concern. Therefore it is only sensible that governments and private industry create and enforce data sanitization policies to prevent data loss or other security incidents.

  1. ^ Cite error: The named reference :92 was invoked but never defined (see the help page).
  2. ^ "K - anonymity: An Introduction". Privitar. 2017-04-07. Retrieved 2021-06-12.

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