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Differential identifiability is a method used to ensure that data cannot be re-identified to individual users, which is crucial when dealing with sensitive information like health conditions. This method can help strengthen privacy controls by applying mathematical techniques to anonymize data while preserving its utility for analysis.

The most effective and efficient way to handle different access levels to credit card numbers within the same application is to obfuscate the credit card numbers whenever a user who does not have the right to see them accesses the data. Option C ensures that sensitive information is masked for unauthorized users without the need for maintaining multiple data copies or complex encryption schemes. This approach aligns with data minimization and access control principles discussed in the IAPP’s CIPT materials, which advocate for minimizing exposure of sensitive data.

Option A: Express consent occurs when an individual takes a specific, observable action, such as signing a document or clicking an "I agree" button online, to give explicit permission for their information to be processed. This type of consent is clear and unambiguous.

Option B: Implied consent is inferred from an individual's actions, such as when they provide information voluntarily without a specific action indicating consent.

Option C: Informed notice refers to providing individuals with information about how their data will be used, but it does not itself constitute consent.

Option D: Authorized notice is not a standard term in data protection and privacy contexts.

References:

IAPP CIPT Study Guide

GDPR Article 4(11) Definitions on Consent

FAIR (Factor Analysis of Information Risk) analysis is a structured approach to understanding, analyzing, and quantifying information risks. The core factors in FAIR analysis include the severity of the harm (option A), the capability of a threat actor (option B), and the probability of a threat actor's success (option D). The stage of the data life cycle, while important in understanding data management practices, is not a direct factor in the FAIR analysis framework. According to IAPP documentation, FAIR analysis focuses on quantifying risk factors to evaluate and manage privacy risks effectively, emphasizing measurable and actionable components rather than the data life cycle stage.