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Frequently Asked Questions
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What is data de-identification?
Data de-identification is the removal of personal information, such as names, specific geographic locations, telephone numbers, and social security numbers, to prevent the identification of specific individuals within a data set. This practice mitigates privacy risks and prepares data for access, analysis, and sharing.
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Why is it important to de-identify data?
De-identifying data preserves individuals’ privacy and enables valuable data sharing and use. De-identification is a core requirement for HIPAA compliance, as it ensures that medical and health data can be used in areas such as research, policy assessment, and comparative effectiveness studies, without compromising the individual’s right to privacy.
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Common masking techniques
- Dynamic Data Masking shields confidential information in production data in real time, without making any physical changes to the data set, and prevents data requesters from accessing the sensitive information.
- Dynamic K-Anonymization automatically anonymizes and hides infrequent, identifiable responses when specific columns are queried.
- Conditional Data Masking uses dynamic access restrictions, based on policy conditions and characteristics, to mask columns, cells, and rows for certain users.
- Randomized Response introduces plausible deniability into data to anonymize specific columns.
- Differential Privacy injects noise into queries to protect the privacy of individual records.