MLA-C01 Question 108
Select 3You are working on a machine learning project for a healthcare company that involves analyzing patient data to predict diseases. The dataset includes sensitive information such as patient names, social security numbers, and medical histories. To comply with data privacy regulations and ensure that sensitive data cannot be re-identified, which of the following techniques should you implement before using the data for training your machine learning models?
- A
Replace patient names and social security numbers with randomly generated pseudonyms.
- B
Remove all sensitive fields like patient names and social security numbers from the dataset entirely.
- C
Apply differential privacy techniques to the dataset.
- D
Encrypt the dataset but keep sensitive fields intact for model training.
- E
Mask sensitive fields like patient names and social security numbers by hashing them with a one-way hash function.
Show answer and explanation
Correct answers: A, C, E
Explanation
In machine learning projects involving sensitive data, it is critical to anonymize or mask sensitive fields to comply with data privacy regulations like HIPAA or GDPR. Techniques like pseudonymization, differential privacy, and one-way hashing ensure that sensitive data cannot be re-identified while retaining its utility for model training. Removing sensitive fields entirely may lead to information loss, and encryption alone does not address the need for anonymization during model training.
- A. Correct.
Replacing sensitive information with pseudonyms ensures that the dataset cannot be directly tied back to an individual, and is a common anonymization technique.
- B. Incorrect.
While removing sensitive fields can protect privacy, it may lead to loss of important features that could be critical for model training. This is not always the best approach in practice.
- C. Correct.
Differential privacy techniques add noise to the data, ensuring that sensitive information cannot be re-identified, making it a strong approach for anonymization.
- D. Incorrect.
Encrypting the dataset only secures it during storage or transmission and does not prevent re-identification when decrypted and used for model training.
- E. Correct.
Hashing sensitive fields with a one-way function ensures that these fields cannot be reversed to their original values, effectively masking them while retaining utility for certain use cases.