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In other cases, such as the NSA report on the USS Liberty incident (right), the report may be sanitized to remove all sensitive data, so that the report may be released to the general public. As is seen in the USS Liberty report, paper documents are usually sanitized by covering the classified and sensitive portions before photocopying the ...
The purpose of metadata removal tools is to minimize the risk of such data leakage. [4] The metadata removal tools that exist today can be divided into four groups: Integral metadata removal tools, which are included in some applications, like the Document Inspector in Microsoft Office. Batch metadata removal tools, which can process multiple ...
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 ...
Avoid clicking on links or downloading attachments from suspicious emails that claim to be by any company connected to a data breach. Use Two-Factor Authentication
Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous.
Data remanence is the residual representation of digital data that remains even after attempts have been made to remove or erase the data. This residue may result from data being left intact by a nominal file deletion operation, by reformatting of storage media that does not remove data previously written to the media, or through physical properties of the storage media that allow previously ...
Companies that handle massive amounts of user data are often the least careful with it. Last year, the National Public Data breach exposed 2.7 billion records.The company’s entire business model ...
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 ]