Privacy-Preserving

Data Privacy by Design in AI Systems Data privacy by design means building AI systems with privacy protections from the start, not as an afterthought. It treats personal data as a core requirement, guiding every decision from data collection to model deployment. This approach helps organizations reduce risk, gain user trust, and meet legal expectations. Begin with a clear data inventory and purpose specification. Define what data is needed for the task, how it will be stored, and when it will be deleted. Apply data minimization and purpose limitation by design. ...

Big Data Privacy Anonymization and Pseudonymization Big data projects often mix millions of records with personal hints. Protecting privacy is essential for user trust and regulatory compliance, but teams still need useful insights. Anonymization and pseudonymization are two core tools to balance privacy and analytics. What they mean Anonymization removes identifiers that can link a record to a real person. It also reduces or hides indirect clues that could help identify someone. Pseudonymization replaces direct identifiers with a stable token. The same person can still be linked across datasets if allowed, but the real name stays hidden. ...

Privacy-Preserving Computation: Federated Learning Federated learning lets devices learn together without sending raw data to a central server. Each device trains a local model on its own data and shares only small updates. The server combines those updates to build a global model. This keeps personal data on the device, reducing exposure and meeting privacy goals. In practice, the process starts with a global model. In rounds, a subset of devices downloads the model, trains for a bit on their data, and sends back updates. The central server averages these updates to form a new global model. This setup works well for mobile apps, smart devices, and services that touch many users. It can be enhanced with privacy tools to further protect individual data. ...