In today's data-driven world, businesses have access to more information than ever before. However, with great power comes great responsibility. Join us for a keynote presentation on how to use data in a way that is both ethical and effective. Learn about the latest trends in privacy-enhancing technologies and discover how you can leverage them to achieve business success while still protecting your customers' privacy.

This session will focus on exploring solutions for cross-border data transfer and data anonymization use cases. The importance of working together and sharing information to enhance efficiency and uncover untapped opportunities cannot be overstated. However, data is often kept isolated and restricted in order to prevent it from being compromised and to comply with various regulations.

Industry professionals will share their experiences on how they have improved collaboration both within their organisations and with external partners. They will also discuss the challenges they have faced and how they have overcome them, as well as the potential for partnerships created by PETs. 

This interactive showcase session focused on successful and unsuccessful PET implementation, where users and partners gather to share their experiences. During this session, you'll hear from in-house representatives and vendors about their challenges and solutions in implementing privacy-enhancing technology in various industries. Divided into three use cases on each day, we will explore PET implementation in real-life scenarios to help you understand the applicability of PET to your organization. By the end of this session, you'll have gained insights into successful and unsuccessful PET implementation strategies and be better equipped to implement PET in your organization.

PETs represent one link in the data release chain. This talk will consider some key questions organizations should ask before deploying PETs as part of their data work flow.  Examples from NIST's Collaborative Research Cycle will demonstrate the tension between data utility, fidelity, and privacy of synthetic data releases with specific regard to the difficulty in modeling diverse subpopulations.