Data anonymization is the process of removing or obscuring personally identifiable information (PII) from data sets. This technique ensures that the data subjects cannot be identified, thus protecting their privacy. For the healthcare organization, this allows the research institution to perform statistical analysis and derive insights from the patient data without compromising individual patient confidentiality. Anonymization directly meets the dual requirements of data utility for research and the protection of PII.

A. Data encryption renders data unreadable without a key. To be useful for analysis, it would need to be decrypted, re-exposing the PII.

B. Data masking is a technique used to achieve anonymization, but anonymization is the broader, more accurate term for the overall process and goal described.

D. Data hashing creates a one-way, non-reversible string. It is used for integrity verification, not for preserving data in a format suitable for analytical research.

1. National Institute of Standards and Technology (NIST). (2010). NIST Special Publication 800-122: Guide to Protecting the Confidentiality of Personally Identifiable Information (PII). Section 4.3, De-Identifying Information, pp. 4-4 to 4-5.

2. European Union Agency for Cybersecurity (ENISA). (2015). Big Data Security and Privacy. Section 4.2.1, Anonymisation, p. 22.

3. Cloud Security Alliance (CSA). (2017). Security Guidance for Critical Areas of Focus in Cloud Computing v4.0. Domain 5: Data Security and Encryption, p. 68.