IAPP CIPT Practice Test - Questions Answers, Page 22

a network threat model helps assess the probability, the potential harm, and the priority of attacks to help minimize or eradicate the threats.

it is important for a privacy technologist to understand dark patterns in order to reduce the risk of manipulation of a user's choice. Dark patterns are user interface design choices that are intended to manipulate users into taking actions they might not otherwise take.

The 'flow' component of Nissenbaum's contextual integrity model refers to how personal information moves within a particular context or domain. This model emphasizes that privacy is maintained when information flows according to norms appropriate to that context. For example, health information shared between a patient and doctor should not be shared outside the medical context without consent.

IAPP CIPT Study Guide, 'Privacy Frameworks and Models,' which discusses Nissenbaum's contextual integrity model and its components, including the flow of information within contexts.

The most important action before collecting personal data directly from a customer is to define the purpose for collecting and using the data. This step ensures that the data collection is justified and that customers are informed about how their data will be used, which is crucial for gaining their trust and compliance with data protection regulations.

IAPP CIPT Study Guide, 'Data Collection and Use,' which emphasizes the necessity of defining the purpose of data collection as a key principle of data privacy and protection.

The Privacy by Design principle that requires architects and operators to emphasize the interests of the individual by offering measures such as strong privacy defaults, appropriate notice, and user-friendly options is 'Respect for user privacy.' This principle ensures that user-centric privacy measures are embedded into the design and operation of systems.

IAPP CIPT Study Guide, 'Privacy by Design,' which outlines the seven foundational principles of Privacy by Design, including respect for user privacy and its emphasis on user-centric measures.

Ryan Calo's Harm Dimensions categorize privacy harms into two types: objective and subjective. Objective privacy harms are tangible, measurable, and involve actual harm to individuals. Receiving spam following the sale of an email address is a concrete, quantifiable harm that directly impacts the individual by causing inconvenience and potential security risks. This contrasts with subjective harms, which are more about perceptions and feelings, such as negative feelings derived from government surveillance (option B). The IAPP documentation reflects this distinction by emphasizing the importance of identifying and mitigating objective harms to ensure robust privacy protections.

Appropriation harms occur when someone's personal information is used without their consent, often for malicious purposes. An unauthorized individual obtaining access to personal information and using it for medical fraud is a clear example of appropriation harm because it involves the misuse of someone's personal data for fraudulent activities, potentially causing significant financial and personal damage to the victim. The IAPP emphasizes that appropriation harms are serious privacy violations that require stringent safeguards to protect individuals' personal data from unauthorized use.

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.