Data Anonymization Vs Data Redaction
2023-10-25![Data Anonymization Vs Data Redaction Data Anonymization Vs Data Redaction](https://varindia.com/storage/news/uploads/2018/02/6538bd05a7e7c.jpg)
Increasing volume of data has raised the concerns about personal data protection and privacy at a global level. It is an ardent necessity for the organizations to implement appropriate technical and organizational measures to ensure personal data security and notify the relevant supervisory authority and affected individuals in case of a data breach. The expanding data economy thrives on greater data privacy compliance.
Data anonymization and data redaction are both techniques used to protect sensitive information, but they differ in their approach and the level of protection they provide.
Data redaction is the process of hiding and protecting sensitive information by using advanced analytics techniques such as Natural Language Processing and Named Entity Recognition. Sometimes Data redaction is also misinterpreted as data anonymization. But in data anonymization, the information is masked, whereas in data redaction the information is completely removed.
Data anonymization involves modifying or removing personally identifiable information from a dataset to protect individual privacy. This process ensures that the data cannot be linked back to specific individuals, while still maintaining its usefulness for analysis and research.
Anonymized data is data that has been altered in a way that makes it impossible, or very difficult, to identify the person associated with it. The process of data anonymization obscures or removes PII (Personally Identifiable Information) from a dataset while ensuring the data remains functional for software business analytics, customer support, development and testing, and other use cases.
Common anonymization techniques includes:
· Generalization: Replacing specific values with broader categories (e.g., replacing exact birthdates with age ranges).
· Suppression: Removing sensitive data fields altogether (e.g., removing names or social security numbers).
· Data masking: Replacing sensitive data with random or artificial values while preserving the data format.
Today, the BFSI sector, Hospitality sector are slowly adopting the Data redaction technology to comply the Data privacy act. It involves selectively removing or obscuring sensitive information from a document or dataset. This technique is often used to protect confidential or classified information while still allowing the release of the remaining content.
The key difference between data anonymization and data redaction lies in the extent of data modification and the purpose of the modification. Data anonymization focuses on protecting privacy by modifying or removing PII, while data redaction aims to protect sensitive information by selectively removing or obscuring it.
Sensitive data must be removed from public view to prevent identity theft and fraud attempts from malicious parties. However, businesses holding extensive database facilities with vast amounts of physical data can have a painfully slow and cost-prohibitive manual editing process.
In summary, data anonymization modifies or removes PII to protect privacy while maintaining data utility, while data redaction selectively removes or obscures sensitive information to protect confidentiality.
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