Ensuring that information remains accurate is the activity that best supports the principle of data quality. Data quality principles emphasize the importance of keeping personal information correct, complete, and up-to-date to prevent harm and ensure reliability. Maintaining accuracy involves regular updates, validation, and correction processes to avoid using outdated or incorrect data (IAPP, Certified Information Privacy Technologist (CIPT) materials).

Option A (Implement a CAPTCHA system): CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) helps to prevent automated bots from attempting unlimited login attempts, reducing the risk of brute force attacks. It effectively adds a layer of security by ensuring that login attempts are made by humans, not automated scripts.

Option B (Server-side input validation checks): While important for overall security, input validation checks do not specifically address the issue of limiting invalid login attempts.

Option C (Enforce strong password and account credentials): Strong passwords and credentials are important but do not directly limit the number of invalid login attempts.

Option D (Implement strong TLS): Transport Layer Security (TLS) ensures encrypted communication but does not limit the number of login attempts.

References:

OWASP (Open Web Application Security Project) guidelines on preventing brute force attacks.

NIST SP 800-63B Digital Identity Guidelines on authentication and brute force protection.

Conclusion: Implementing a CAPTCHA system (Option A) is the best recommendation to minimize the potential privacy risk from unlimited invalid login attempts, as it helps to distinguish between human and automated login attempts, reducing the risk of brute force attacks.

Perturbation is the preferred technique for protecting medical data while still allowing for analysis. Perturbation involves adding noise to the data to prevent the identification of individuals while preserving the overall patterns and trends in the dataset. This method is particularly useful in data mining for research purposes where privacy concerns must be addressed. The IAPP’s CIPT materials cover this technique under data anonymization and de-identification strategies, emphasizing its importance in research and analytics.