Comparison of Privacy Enhancing Technologies (PETs)
This comparative reference chart covers seven PETs — Differential Privacy, Synthetic Data, Federated Learning, Trusted Execution Environments, Secure Multi-Party Computation, Homomorphic Encryption, and Zero-Knowledge Proofs — across six dimensions: approach, benefits, limitations, example use case, data utility impact, and implementation complexity. The chart is designed as a standalone reference for practitioners who need to quickly […]
Privacy Enhancing Technologies for EdTech Service Providers
Educational research answers questions about program effectiveness, equity, access, and long‑term outcomes. EdTech vendors typically need student-level data and enough context to ensure their research is effective and accomplishes their goal. At the same time, disclosing student data to EdTech vendors may increase the risk of reidentification. The risk is not limited to direct identifiers […]
Privacy Enhancing Technologies for Education Researchers
Educational research answers questions about program effectiveness, equity, access, and long‑term outcomes. Researchers typically need student-level data and enough context to ensure their research is effective and accomplishes their goal. At the same time, disclosing student data to researchers may increase the risk of reidentification. The risk is not limited to direct identifiers such as […]
Privacy Enhancing Technologies for State Education Agencies
This report describes the Privacy-Enhancing Technologies (PETs) most relevant to State Education Agencies (SEAs) and Statewide Longitudinal Data Systems (SLDS) environments, explains what each can and cannot do analytically and operationally, and provides use case guidance for matching PET selection to specific workflows. PETs complement rather than replace strong governance — data minimization, least-privilege access, […]