What technology is increasingly being utilized to enhance privacy and security in AI data processing?

Federated learning stands out as a technology increasingly adopted to enhance privacy and security in AI data processing. This approach allows machine learning models to be trained across multiple decentralized devices or servers holding local data samples, without transferring that data to a central server. This design is fundamentally significant for privacy because it minimizes the risk of exposing sensitive data while still allowing for collaborative learning.

In federated learning, local models are trained on-device and only model updates (such as gradients) are sent to a central server, which aggregates these updates to form a global model. This means that individual data remains localized, thus significantly reducing the potential for data breaches or unauthorized access.

The other options, while relevant in different contexts, do not primarily focus on enhancing privacy and security in the way federated learning does. Blockchain technology is primarily geared toward secure transactions and not specifically about training models with privacy in mind. Containerization helps with application deployment efficiency but does not directly relate to privacy enhancement. Quantum computing, although promising for processing power, addresses speed of analysis rather than focusing on the preservation of privacy in the context of data handling and training AI models.