Privacy by Design and Default in AI: The Role of Privacy-enhancing Technologies

Bojana Bellamy, CIPP/E, President, Centre for Information Policy Leadership

Derek Ho, CIPP/US, CIPM, Senior Vice President, Assistant General Counsel, Privacy and Data Protection, Mastercard

Chein Inn Lee, Deputy Director, Development of Data Driven Tech, IMDA

Christian Reimsbach Kounatze, Information Economist and Policy Analyst, OECD

As AI continues to evolve, privacy concerns surrounding the collection and processing of personal data in AI systems, especially in models like generative AI, are growing. Privacy-enhancing technologies hold immense potential to help implement privacy by design and by default when developing AI systems. In this panel, experts will explore how PETs, such as federated learning, differential privacy and homomorphic encryption, can strengthen privacy protection and drive innovation in AI. CIPL will also share insights from its paper, "PETs and PPTs in AI: Operationalizing Privacy by Design and Default," outlining how these technologies address privacy risks and create new opportunities in data sourcing, model training, security and collaboration.

What you will learn:

• Real-world examples of how PETs such as differential privacy, federated learning and homomorphic encryption can enhance privacy and foster innovation. 
• Key challenges companies face in implementing these technologies and strategies for overcoming them, including insights into the regulatory and ethical landscape. 
• What actionable steps can be taken to encourage widespread adoption of PETs and advance a more secure, trustworthy AI ecosystem.