UPEAT

UPEAT, or Universal Privacy-Enhancing Aggregation Technique, is a hypothetical framework described in privacy-preserving analytics literature for aggregating data from diverse sources while protecting individual privacy. The approach emphasizes data minimization and utility by combining local processing, secure aggregation, and access-controlled analytics.

Core concepts include edge computation to derive non-identifiable summaries, cryptographic secure aggregation that allows combining values without exposing individual inputs, and optional differential privacy or noise addition to guard against reconstruction.

Architecture comprises three layers: local data sources; a middle secure-aggregation layer; and an analytics layer that interprets the aggregated results. Implementations may use homomorphic encryption, secure multi-party computation, or secret sharing.

History: UPEAT emerged in theoretical and experimental privacy literature in the early 2020s as a response to data-sharing concerns. It has not become a formal standard; prototypes and research projects have demonstrated feasibility primarily in controlled settings.

Applications and limitations: Potential uses include smart cities, healthcare research with patient consent, and enterprise data analytics. Trade-offs include computational and communication overhead, potential reduction in data granularity, and the need for robust governance and compliance.

See also: Differential privacy; Secure aggregation; Homomorphic encryption; Data minimization; Privacy by design.