Integrate PETs at every stage of the research lifecycle¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The short-term recommendations advise researchers conducting research with DLPs to look for ways to integrate PETs into all stages of the research lifecycle: secure one-way identifiers at data collection, synthetic data for testing code at data cleaning/prep, federated learning during modeling, differential privacy for output reporting, and privacy preserving record linkage or secure multiparty computation for data sharing. The article also suggests PETs can lower barriers to data use, for example by supporting a doctoral student with a public use file while waiting for a data-use-agreement.

Design Implications¶

Context¶

Requirements¶

• Organizational PET literacy built through trainings, speakers, and pilot studies

Constraints¶

• DLP data can be difficult to de-identify and integration with multiple systems compounds re-identification risk

Target Learners¶

• researchers using digital learning platforms
• doctoral students awaiting data-use agreements

Target Learning Goals¶

• privacy-protective research practice across the data lifecycle

Related Strategies¶

• Five recommendations for integrating Improvement Science with SEERNet
• Move from extractive research toward research-practice partnerships, practitioner-facing outputs, and transparent privacy documentation
• Use an operationally separate honest broker to perform linkage and de-identification

Examples¶

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Key Sources¶

• Dorie, Vincent. (2025, December). Privacy enhancing technologies in digital learning platforms. Digital Promise. https://doi.org/10.51388/20.500.12265/278