Again in groups, attendees will be presented with a series of often faced challenges that potential PETs adopters will encounter and discuss best practices for mitigating them. Attendees will take part in one discussion from round A and one from round B.

Round A:

• Handling and overcoming internal pushback
• Identifying use cases and implementing in practice
• Determining core privacy challenges

Round B:

• Effectively utilising and maximising the potential of synthesised data
• Establishing data-sharing initiatives with market competitors
• Leveraging PETs to convince customers their data is safe

In intimate groups filtered by industry, all attendees will discuss the use cases, challenges and opportunities they have experienced when looking to implement PETs. Attendees will also seek to identify the single most pressing privacy or data concern they currently face and how PETs could be a solution, before presenting back to the wider room to encourage cross industry benchmarking.

Groups to be split into:

• Finance
• Healthcare and Pharmaceuticals
• Manufacturing
• Marketing, Advertising and Social Media
• Transport, Shipping and Logistics
• Hospitality
• Aerospace and Defence
• Public Sector

Businesses must be able to trust PETs internally to allow their deployment upon highly sensitive and

regulated data. Customers knowing their data is safe would help shift the current stance

of default scepticism and encourage the voluntary supply of additional data- benefiting both parties.

• How to build trust internally, moving from proof of concept to quantifiable measures of success or failure.
• Determining and accepting that no technology is perfect and what an acceptable level of risk looks like in practice
• How to communicate to customers that their data is now safe as a result

Artificial Intelligence and Machine Learning can allow companies to gain greater insights on existing data and explore new ways for that data to then be utilised. PETs can aid this process during development, implementation and monitoring.

• Synthetic data as a means by which to train AI/ML models- the pros, cons and achieving the best result in practice
• Utilising AI/ML in conjunction with PETs to help achieve a proactive rather than reactive data stance, giving organisations greater insight to the data they store and how it is used
• Strengthening the ability of enterprise to comply with various requirements from regulators, customers and internal stakeholders.

What is the common point between 19th century British biology researchers and today's Swiss data science experts? Come and find out!

Leadership in data science is a target for many of us. Building that leadership means delivering results. In this session you will learn about data projects in the healthcare industry, the technologies that supports them, the challenges we faced, and the friends we made along the way.

We will also discuss the financial longevity of data science projects and the cultural resistances that have been faced since more than 50 years in that field

One of the key capabilities of PETs provide is the ability to anonymise data, allowing for that data to be shared internally and externally in full compliance without risk of breach. This panel will focus on the subsequent benefits and practical steps for implementation.

• Retaining data utility and quality after anonymisation
• The implications of pseudonymised vs anonymised data
• Building the infrastructure required for internal and external data sharing initiatives