Understanding the differences between Data Privacy and Data Security
This is the first blog in our Crash course in Privacy series
Privacy is all over the news these days, from Facebook scandals to European fines associated with failing to comply with GDPR. This is caused in part because protecting the privacy of your customer’s data is a complex issue that requires an understanding of two very important terms that are often used interchangeably: Privacy and Security.
“Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction.” Source Carnegie Mellon University. It encompasses network security, physical security, and file security. Some standard techniques to secure data are encryption, multi-factor authentication, and access controls. Encryption encodes data so that only authorized users can decrypt it with an encryption key. Multi-factor authentication requires users to provide two or more pieces of evidence that prove they have permission to access the data. Access controls restrict users ability to access data until they have provided the correct credentials. Creating a comprehensive data security policy is critical, but it is not sufficient because:
- Breaches can occur when the standard techniques fail. For example, if the encryption key was obtained or if unauthorized access occurred as in the case of the Marriott data breach.
- The standard techniques for securing data makes it difficult and in some cases impossible to extract analytical value from the data.
- Analysis of encrypted data is not practical, therefore organizations decrypt and the data becomes exposed during analysis.
Data Privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization. Privacy regulations, like GDPR, refer to two different privacy measures that can be used to protect privacy:
- Pseudonymization – a data management procedure by which personally identifiable information(PII) fields within a consumer data record are replaced by one or more artificial identifiers, or pseudonyms, and can be recalled at a later date to re-identify the record.
- Anonymization – the process of removing any identifiable information from consumer data such that individuals are no longer re-identifiable.
The key to managing data privacy is understanding the trade-off between protecting privacy and retaining analytical value. The techniques to protect privacy transform the original data by making it more general. The more general the data becomes the less useful it becomes for analysis, but the more protected it is from re-identification. It is important to have a quantifiable measure of how these techniques impact the analytical value of your data.
Traditionally, organizations have focused more on security than privacy, locking data behind passwords and access control. However, to fully protect the data, organizations need to consider a combination of privacy and security techniques that help them comply with regulations, protect privacy, reduce the risk of consumer exposure, and increase ROI on their digital strategies.
The other blogs in the Crash course in Privacy series are: