Part 16/19:
Privacy-enhancing technologies (PETs) are designed to protect personal information in our data-driven world. They include advanced encryption methods and synthetic data generation. Homomorphic encryption allows computations on encrypted data without decrypting it first, potentially revolutionizing fields like healthcare by enabling analysis of sensitive medical data without compromising patient privacy. Federated learning allows AI models to be trained on distributed data sets without centralizing data, already used by companies like Google to improve keyboard predictions without accessing users' personal messages. PETs could enable new forms of collaboration and data sharing, unlocking previously inaccessible insights due to privacy concerns. However, many of these technologies are computationally intensive, potentially slowing down systems or increasing costs.