Data Protection and Privacy Summit

Exposing sensitive data for analysis is no longer necessary. Privacy-enhancing technologies, such as homomorphic encryption and federated learning, now enable organisations to collaborate on valuable data assets while maintaining full control over access and visibility.

Picking the right privacy-enhancing technology is a strategic decision. The wrong choice can undermine data utility or leave critical gaps in protection. In this session, Obuks Akpede, Data Governance Consultant at Adastra Corporation, outlines a practical roadmap to help you align PET selection with business risk, data sensitivity, and operational requirements, so you can minimize exposure without sacrificing value.

Key Takeaways
- Align PET selection with business risk, data sensitivity, and performance needs.
- Determine where homomorphic encryption, secure multi-party computation, differential privacy, or federated learning best fit your use cases
- Quantify privacy-utility trade-offs and define targets for latency, cost, accuracy, and privacy budgets.
- Embed governance and operating model principles for minimization, access control, retention, approvals, and auditability from the outset.
- Reduce re-identification and exposure risk while preserving data utility and time-to-insight.
- Apply a structured implementation playbook from pilot to production, with clear KPIs, SLAs, and checkpoints.