Phase 2 Detailed Plan: Metrics Definition and Data Preparation
Timeline target: Month 2
Goal
Define measurable privacy-risk metrics across user, system, and organization levels, then validate feasibility using synthetic and public data.
Scope
In scope:
- Metric formula/spec design.
- Synthetic data generation for controlled testing.
- Public breach data alignment (ENISA, PRC references).
- Conceptual scoping for digital ecosystem indicators.
Out of scope:
- Final production calibration across all industries.
- Full ecosystem-level implementation.
- Phase 1 indicator catalog and traceability matrix.
- ENISA and PRC breach datasets.
- Proposed metric dimensions from proposal.
Steps to Complete
- Define metric schema
- For each indicator, define:
- metric id,
- formula,
- required fields,
- normalization rule,
- confidence weighting,
- missing data handling.
- Build synthetic data generator
- Create representative entities: users, systems, vendors, incidents.
- Inject controlled edge cases (missing consent records, delayed breach response, weak safeguards).
- Create dataset variants for normal, stressed, and adversarial conditions.
- Integrate public breach context
- Map ENISA/PRC attributes to internal schema.
- Add transformation pipelines with data quality checks.
- Prototype scoring at three levels
- User-level metrics (control/consent exposure).
- System-level metrics (encryption posture, sharing exposure).
- Organization-level metrics (response time, safeguard maturity).
- Define draft composite scoring strategy
- Choose aggregation method (weighted sum, Bayesian prior-informed, or hybrid).
- Document score interpretation bands (low/medium/high risk).
- Document ecosystem-level future scope
- Capture cross-border transfer and interoperability indicators as future implementation backlog.
- Freeze Phase 2 baseline
- Publish metric spec, synthetic data dictionary, and baseline results.
Deliverables
- Metric specification document.
- Synthetic data generation scripts and data dictionary.
- Public-data mapping report.
- Baseline metric result snapshots.
- Ecosystem scope note.
Recommended Acceptance Checks
- Every Phase 1 indicator maps to a metric or has explicit deferral rationale.
- Synthetic data covers normal and edge-case scenarios.
- Metric outputs are numerically stable and interpretable.
- At least one end-to-end run from raw input to scored outputs is reproducible.
Risks and Mitigations
- Risk: Synthetic data does not reflect real patterns.
- Mitigation: Seed distributions from public breach statistics where feasible.
- Risk: Overly complex metric formulas.
- Mitigation: Start simple, add complexity only if it improves explainability.
- Risk: Missing fields in public datasets.
- Mitigation: Add imputation policy and confidence penalties.
Recommended Week-by-Week Breakdown
Week 1:
- Finalize metric schema and formula drafts.
Week 2:
- Build synthetic generator and produce first datasets.
Week 3:
- Add public-data mappings and run baseline scoring.
Week 4:
- Review outputs, refine formulas, freeze Phase 2 artifacts.
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