PETs as dual technical and social technologies for protecting data while preserving utility¶

Theory · All theories
Evidence · 2 claims (1 for, 1 against) · 2 studies (2 theoretical), q1 · 0 of 2 report an effect size · 2 claims rest on one study

Description¶

The article defines Privacy Enhancing Technologies as tools that aim to increase the protection of individual data while retaining its utility. It frames them as both technical tools (e.g., math and software) and social technologies that reshape governance, trust, and data-sharing practices, noting that many can be thought of as purpose-limiting technologies that reshape data so it can only be used for specific ends. This framing underpins the brief's argument that PETs belong in data governance, not just engineering.

Design Implications¶

Context¶

Requirements¶

• Institutional adoption requires stakeholder buy-in and clear communication of benefits

Constraints¶

• PETs alone are rarely sufficient to grant compliance with relevant regulations

Target Learners¶

• students using digital learning platforms, many of whom are minors

Target Learning Objectives¶

• responsible protection and use of learner data in educational research

Claims¶

• PETs expand the value of data by enabling sharing where in-the-clear approaches are infeasible [+W]
• PETs trade utility for privacy, limiting use where accuracy is mandated [-W]

Related Theories¶

• Taxonomy of PETs by input privacy versus output privacy

Examples¶

• End-to-end PET workflow: SMPC linkage of DLP engagement data with state college outcomes
• Integrate PETs at every stage of the research lifecycle
• Invest in technological infrastructure for evidence generation, including synthetic data, shared platforms, and privacy-enhancing technologies

Key Sources¶

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