privacypreserving

Privacy-preserving refers to methods and practices that protect individuals' personal information throughout the data lifecycle, including collection, storage, analysis, and sharing. The goal is to minimize disclosure risk while enabling useful data use.

Core concepts in privacy-preserving approaches include data minimization, purpose limitation, consent, transparency, and security. These approaches aim to balance data utility with protections, often by transforming data or controlling access rather than relying solely on strict prohibitions.

Common techniques include differential privacy, which adds controlled noise to outputs; secure multi-party computation, enabling joint computation without exposing inputs; homomorphic encryption, permitting computation on encrypted data; federated learning, where models are trained locally and aggregated; and trusted execution environments that isolate computations. Data anonymization methods such as k-anonymity, l-diversity, and t-closeness are used in some contexts but have limitations against sophisticated attacks.

Applications span statistics, healthcare, finance, marketing analytics, and open data portals. Real-world use includes releasing aggregate statistics without identifying individuals, enabling private data collaborations across organizations, and training models while keeping raw data on-site.

Challenges include maintaining data utility while preserving privacy, risks of re-identification, measuring privacy loss, scalability, and regulatory compliance. The choice of technique depends on the data type, threat model, and intended use.

See also privacy by design, information privacy, GDPR, differential privacy, secure multi-party computation, homomorphic encryption, and federated learning.