The Future of Privacy Forum (FPF) has released a suite of practitioner resources on Privacy Enhancing Technologies (PETs) for the education sector. Building on FPF’s 2025 landscape analysis of PETs adoption by State Education Agencies, the new resources move from landscape analysis to implementation considerations — providing audience-specific guidance for the three practitioner communities most responsible for handling student data: state education agencies and statewide longitudinal data systems, education researchers, and EdTech vendors.

FPF worked with AEM Corporation to develop the resources, which include three practitioner guides and a comparative reference chart covering seven PETs relevant to education data environments.

Addressing a Gap Between Awareness and Practice

FPF’s 2025 landscape analysis found that awareness of PETs among education practitioners remains limited, and that even practitioners who understand what PETs are often lack the use case guidance needed to match a specific technology to a specific workflow. The new guides are designed to close that gap. Each is written for its audience’s actual decision context —as a practical resource for the people who manage longitudinal data systems, design research studies, or build and test EdTech products using student data.

“State education agencies, researchers, and EdTech vendors all work with student data, but they face different risks, different analytical requirements, and different governance obligations,” said Jim Siegl, FPF Senior Fellow for Youth & Education Privacy. “These guides are designed to help each audience understand not just what PETs can do, but what each approach costs analytically — and how to make and document those tradeoffs responsibly.”

What the Guides Cover

Privacy Enhancing Technologies for State Education Agencies: A practical guide to privacy-preserving computation for state education data systems addresses the specific challenges of SEA and SLDS environments, where linked longitudinal records create both high analytical value and elevated re-identification risk. The guide explains how PETs can reduce how often student-level data must be copied, moved, or distributed to support analysis, and provides use case guidance for cross-agency computation, public reporting, and research partnerships. It also addresses a tension that is particularly acute in state education data: the student populations most at risk of re-identification — small districts, low-incidence disability categories, and rare demographic combinations — are often those for whom noise-based methods like Differential Privacy perform least well analytically.

Privacy Enhancing Technologies for Education Researchers: A practical guide to conducting education research with reduced data exposure addresses the analytical tradeoffs researchers need to understand before selecting a PET for a given study. Results produced under Differential Privacy carry an epsilon parameter that should be reported. Synthetic data findings require disclosure of generation methodology and fidelity validation. The guide frames PET selection as a methodological decision with implications for replication and publication, not just a data governance requirement.

Privacy Enhancing Technologies for EdTech Vendors: A practical guide to handling student data across product, testing, and analytics workflows addresses the range of vendor workflows — system testing, staff training, product analytics, and collaborative research with agencies — that involve student data and carry different PET requirements. The guide emphasizes that vendors operate under a dual obligation: to deliver useful analytics and product capabilities, and to handle student data in ways that honor the trust schools and agencies have placed in them. It includes guidance on transparency with agency partners when PET-protected outputs are shared, including disclosure of noise parameters and fidelity limitations.

The Comparison Chart

Accompanying the three guides is a comparative reference chart covering 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 orient to the PET landscape or compare options for a specific workflow, without reading all three guides in full.

Selecting the Right PET

A consistent theme across all three guides is that PET selection is a methodological decision, not a compliance checkbox. Each approach involves a tradeoff between privacy protection and analytical precision, and that tradeoff varies by method and by context. Differential Privacy introduces noise that grows more distorting as group sizes decrease. Synthetic data may misrepresent rare populations. Secure Multi-Party Computation and Trusted Execution Environments constrain which analyses can be run. Federated Learning reduces raw data exposure but can produce less accurate models when district data is heterogeneous.

The guides encourage practitioners to identify the acceptable level of analytical imprecision for their specific workflow before selecting a PET, to document that choice and its rationale, and to disclose relevant parameters — such as epsilon values for Differential Privacy or fidelity validation results for synthetic data — where outputs are shared or published. PETs work best when integrated into existing data governance frameworks rather than treated as standalone solutions.

FPF has actively contributed to shaping policy and practice around PETs through discussion papers, reports, stakeholder engagement, and its PETs Repository, launched in November 2024 as a centralized resource for practitioners seeking practical information about these technologies. The new practitioner guides extend that work by providing the audience-specific implementation guidance the landscape analysis identified as a critical gap.