Ace Your CIPT Information Privacy Technologist Exam

The fundamental principles of information security are often summarized by the CIA triad, which stands for Confidentiality, Integrity, and Availability. Confidentiality ensures that information is not disclosed to unauthorized individuals, entities, or processes. It is crucial in protecting personal and sensitive data from unauthorized access and breaches. This principle is widely recognized and referenced in various information security standards and frameworks, such as ISO/IEC 27001 and NIST SP 800-53.

The primary privacy consideration for not sending large-scale SPAM type emails to a database of email addresses is that these emails are unsolicited. Option B highlights this concern, as unsolicited emails can violate privacy principles and regulations such as the General Data Protection Regulation (GDPR) and CAN-SPAM Act, which require consent for marketing communications. This point is well-documented in IAPP’s CIPT resources, particularly in discussions about user consent and data protection in marketing activities.

Calo’s Harm Dimensions categorize privacy harms into two main types: objective and subjective harms.

Identity Theft (Objective Harm): This is a measurable harm that occurs when an individual's personal information is stolen and used fraudulently, leading to financial loss, legal consequences, or other tangible damages.

Embarrassment (Subjective Harm): This is a harm that affects an individual's feelings, emotions, or social standing. It occurs when personal information is exposed in a way that causes humiliation or distress.

These two examples illustrate the categories effectively:

Identity theft represents the objective harm.

Embarrassment represents the subjective harm.

References: IAPP Certification Textbooks, Section on Privacy Harms and Risk Assessment, Calo’s Harm Dimensions.

To ensure privacy when releasing aggregated data, adding noise to the data is a common and effective technique. Noise addition involves introducing random data to the dataset, which helps to obscure individual entries and prevent re-identification. This method maintains the utility of the dataset while protecting the privacy of individuals whose data is included.