Data Minimisation

Definition

Data Minimisation is the study and design of how people establish, exercise, verify, delegate, constrain, revoke and recover control within digital wallets and personal data.

Why this matters

Failures in data minimisation can expose people to identity fraud, unauthorised decisions, coerced data use, loss of access, unfair exclusion, surveillance and diminished human agency.

Research questions

How can data minimisation be designed and governed so that identity, consent and authority remain accurate, understandable, revocable, contestable and aligned with the person’s actual intentions?

Hypotheses

A purpose-limited, evidence-backed and revocable approach to data minimisation, with explicit authority boundaries and human-readable controls, will improve user agency, reduce misuse and increase decision legitimacy compared with opaque, bundled or non-revocable approaches.

Proposed methods

policy and standards analysis; consent-flow mapping; identity and access-control testing; user research; threat modelling; adversarial simulation; usability and comprehension testing; audit-log analysis; rights-impact assessment; methods adapted specifically to Data Minimisation

Stakeholders and beneficiaries

individuals; families; workers; consumers; platforms; governments; identity providers; regulators; legal practitioners; financial institutions; health and social services