Why Protecting Sensitive Data is Important
This is the second blog in our Crash Course in Privacy series
Privacy risk is the probability of extracting information about a specific individual in a data set. Organizations must protect the significant personal information they have from exposure.
Governments around the world have been very active in making sure that consumer privacy is protected by publishing regulations that dictate how the data must be handled and used. These regulations include HIPAA, GDPR, CCPA, PIPEDA etc. The consequences of not complying with these regulations are fines, lawsuits, and reputational damage.
Organizations find themselves trying to answer this question:
How can I comply with privacy regulations & protect consumer privacy while leveraging my data assets for business purposes?
The answer is contained in the regulations:
- HIPAA: The Health Insurance Portability and Accountability Act (HIPAA) is American legislation that requires the protection of 18 specific identifiers: name, Social Security Number, Health Insurance Numbers, and others. Once the dataset has been protected by anonymizing or de-identifying, it can be used for analysis. (Source)
- GDPR: The General Data Protection Regulation is a privacy regulation that has to be observed by any organization that has information about European citizens. GDPR contemplates two ways in which privacy can be protected, pseudonymization, and anonymization. When a dataset is anonymized, GDPR no longer applies to it. (Source)
- CCPA: The California Consumer Privacy Act regulates what each person’s rights are regarding their data. Specifically, CCPA is concerned with information that could reasonably be linked, directly or indirectly, with a particular consumer or household. Data that has been aggregated or de-identified is excluded from the CCPA. (Source)
In light of these regulations and consumer expectations for privacy protection, it is clear that organizations must enact privacy policies. Organizations need to embrace privacy and find a way to embed it into their analytic process if they want to extract value from sensitive data without facing any consequences.
The other blogs in the Crash Course in Privacy series are:
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