CIPT Dumps with Practice Exam Questions Answers

Sophisticated Access Management (AM) techniques focus on controlling who can access certain data and under what conditions. Techniques such as restricting access based on location (A), user role (B), and device type (C) are common in access management systems. However, preventing data from being placed in unprotected storage (D) falls more under data security and protection measures rather than access management. AM primarily addresses the question of who has access and how, whereas ensuring that data is stored securely involves encryption, secure storage solutions, and proper configuration management, which are typically beyond the scope of AM systems. This distinction is made clear in various security and privacy guidelines, including those provided by the IAPP and the National Institute of Standards and Technology (NIST).

 Encryption of Data in Transit: Encrypting health records during transfer is a critical security measure to protect data from interception and unauthorized access. Failure to do so exposes sensitive personal health information to potential breaches.

 Privacy Risks: Not encrypting data in transit can lead to significant privacy breaches, especially when dealing with highly sensitive health information. It is essential to use strong encryption methods to secure data during transfer between users' devices and servers.

 Reference: The IAPP’s documentation on Privacy by Design emphasizes the necessity of encryption for protecting personal data, particularly in healthcare applications where the risk and impact of data breaches are high. Additionally, the Health Insurance Portability and Accountability Act (HIPAA) requires encryption of electronic protected health information (ePHI) in transit to ensure its security and confidentiality.

Data aggregation involves combining data from multiple sources to create a comprehensive dataset. One of the main benefits of data aggregation is that it can help achieve de-identification and unlinkability. By aggregating data, individual data points are merged into broader categories or summaries, reducing the risk that specific individuals can be identified from the dataset. This is particularly useful in privacy contexts where protecting individual identities is paramount. Aggregated data can provide valuable insights while maintaining privacy and security standards.

Deep fakes pose a significant challenge to data protection primarily due to their potential to create and spread highly realistic but false information. According to the accuracy principle of data protection, personal data should be accurate and kept up to date. Deep fakes violate this principle by generating false representations of individuals, leading to potential harm and misinformation. This aligns with the guidelines provided in IAPP documentation that emphasizes the importance of maintaining accurate and truthful personal data to protect individuals' privacy and prevent harm.

To effectively prevent phishing attacks while minimizing data retention, an application server should keep limited-retention logs that are de-identified and include critical metadata, such as the links clicked in messages. This approach helps in tracking potentially malicious activities (like phishing attempts) without retaining excessive personal information that could itself pose a privacy risk. By focusing on metadata and the behavior (links clicked), the server can monitor and mitigate phishing risks while adhering to privacy principles of data minimization and purpose limitation, as recommended by IAPP.