Information Privacy Technologist CIPT Passing Score

When evaluating Machine Learning (ML) solutions, the first step from a privacy perspective is to define how data subjects may object to the processing. This aligns with the principles of transparency and individual rights under data protection laws such as GDPR, which stipulates that data subjects should have the ability to object to the processing of their personal data. This ensures that individuals maintain control over their personal information and that the organization respects their privacy rights. (Reference: IAPP CIPT Study Guide, Chapter on Privacy in Technology and GDPR)

Referential integrity is a database concept that ensures the validity of relationships between data points in different tables but does not directly address data confidentiality. The methods that contribute to data confidentiality include differential privacy, homomorphic encryption, and k-anonymity, as these techniques are specifically designed to protect the privacy and confidentiality of the data subjects. The IAPP emphasizes that confidentiality involves measures to prevent unauthorized access and disclosures, which referential integrity does not inherently provide.

Deep learning, a subset of machine learning, has enabled the greater implementation of machine learning by significantly enhancing the capabilities of neural networks. Here’s how:

Neural Networks Expansion: Deep learning involves the use of large, complex neural networks that have many layers (hence the term "deep"). These networks can model intricate patterns and representations in data.

Massive Data Processing: Deep learning algorithms require and utilize vast amounts of data to train these neural networks. The more data processed, the better the model can learn to generalize and perform accurately on new data.

Automatic Feature Extraction: Unlike traditional machine learning methods that often require manual feature extraction, deep learning algorithms can automatically learn and extract features from raw data. This eliminates the need for hand-coded classifiers and simplifies the process of implementing machine learning models.

Performance Improvements: The ability to process and learn from large datasets has led to breakthroughs in various fields such as image and speech recognition, natural language processing, and autonomous driving.

 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.