What happens when you bring four experts together in a virtual room to discuss Privacy Enhancing Technology?
The short answer: a comprehensive discussion on balancing data privacy, healthcare innovation, and ethical data collection.
On July 10th, Partisia co-hosted an hour-long webinar with Data, Privacy, and Innovation. Titled "Enabling Collaborative Ecosystems by Computing on All the World’s Data," discussing key topics such as:
The use of encryption in the healthcare industry to protect data and facilitate collaboration between hospitals and organisations.
The future of data ownership, emphasising how individuals can control their personal data but still share it securely and transparently.
Making Privacy Enhancing Technologies (PETs) user-friendly for organisations by exploring standardisation efforts and offering guidance on selecting and implementing PET solutions in business settings.
In today’s data-driven world, being able to share data and information between different organisations is crucial for product development and innovation, but also efficiency.
However, ensuring privacy, security and compliance while using this same data is at least equally important.
Please note that this is only a summary of the webinar and does not include a full transcript. To access all the details and information discussed, please watch the video available at the bottom of this page.
Mark our CPO opens the webinar by introducing an overall of what we do at Partisia and how our platform works.
The presentation started by asking the audience this: How do we make sure that when we tell you that we should be able to use all this data, how do we at the same time guarantee security and protection? How do we make sure that privacy and regulation is really there? …And most importantly how do we confidentially compute this data?
Balancing Privacy and Transparency
One of the primary concerns in data utilisation is balancing privacy with productivity. Data must remain protected both in transit and at rest. Confidential computing, which allows computations to be performed on encrypted data, ensures that privacy is preserved throughout the process. By combining privacy preservation with the ability to perform computations on data, Partisia ensures that sensitive information remains confidential while still being useful for analysis and development.
When using data, transparency in its use is paramount. Users need to understand why and how their data is being utilised. Partisia's platform provides mechanisms to ensure this transparency, proving that privacy is maintained while enabling confidential computing.
This dual focus on confidentiality and transparency ensures that data is used responsibly and ethically.
Partisia is very much involved in the field of Decentralised Identity, particularly in compliance with European laws. The digital wallet and verifiable credentials system allow individuals to prove their identity cryptographically without revealing personal information. This approach ensures that only the necessary information is shared, maintaining privacy while enabling secure identity verification.
The technology behind Partisia's platform is built on Multi-Party Computation (MPC).
This technology was developed in the 1980s and has now become viable for widespread use due to advancements in technology and regulation. Partisia leverages MPC to enable secure data sharing, providing predictions for product development, creating value from data that is typically inaccessible due to privacy concerns.
The Three Pillars of Neutral Infrastructure
Partisia's platform is built on three core principles: confidentiality, integrity, and availability.
Confidentiality: Ensuring that data is protected and encrypted, complying with GDPR and other regulatory requirements. If we can combine this we have confidentiality.
Integrity: Providing mechanisms to prove that data remains unchanged and untampered, aligning with AI and Data Acts that demand transparency and accountability.
Availability: Ensuring data is accessible and decentralised.
Kenneth Forstrøm Nielsen from Roche showed how this is brought to life in the OSCAR Project. A major project for sustainable data utilisation in the healthcare industry.
Partisia is part of the OSCAR project, a collaboration involving multiple companies to create a one-stop shop for clinical research utilising Danish health data. The project aims to streamline data access while ensuring security and compliance, allowing for faster, more efficient research without compromising on privacy.
The project brings together companies, with Partisia being one of the key players. The core objective is to establish framework agreements with various Danish data holders, such as Statistics Denmark and the Danish Health Data Authority. This collaborative effort aims to simplify and expedite the process of accessing crucial data, all while ensuring stringent security measures to prevent unauthorised data sharing.
Key Partners
Data Fair
Partisia
Enversion
Statistics Denmark
Danish Health Data Authority
Tværspor
The controlled system ensures that while users can obtain the results necessary for their research, they do not have access to the confidential information underlying those results. This approach balances the need for data accessibility with the imperative of maintaining data privacy and security.
We are excited to take this significant step forward in the field of clinical research and look forward to the positive impact it will have on healthcare innovation.
If you want to read more about the OSCAR-project, click here.
Questions raised by the audience that was discussed:
Several regulations are shaping the Privacy-Enhancing Technologies (PETs) landscape, compelling companies to ensure compliance. These regulations are emerging from various sectors, particularly within the European Union (EU), and are addressing digital infrastructure alongside industry-specific mandates in finance and healthcare.
Key Regulations Impacting the PETs Environment:
General Data Protection Regulation (GDPR):
The GDPR is significantly influencing data protection practices worldwide. It emphasises accountability, transparency, and the safeguarding of personal data.
AI Act and Data Act:
These regulations underscore the necessity for transparency and accountability in the use of artificial intelligence and data management. Companies must be able to explain their data handling practices and ensure compliance with these standards.
Cybersecurity Acts and Resilience Acts:
In response to the global trend toward de-globalization, these acts focus on enhancing cybersecurity and infrastructure resilience. They significantly impact how companies build systems that enable secure data sharing.
Our most important task at the moment, is to find ways to explain our work so that people can understand it.
MPC (Multi-Party Computation) is not just for individual use by private persons and companies; it also facilitates collaboration between companies. This technology allows us to work together without breaching privacy.
This has been a significant change. In the EU, laws are evolving to support this shift, but in some parts of the world, these changes are happening even before the laws catch up. It's crucial to understand the application and importance of these technologies for companies without delving too deeply into the technical details.
For companies looking to adopt PETs, it's crucial to design collaborative frameworks that add value across ecosystems. This involves educating stakeholders about the benefits and use of these technologies and ensuring that the right tools are used to meet specific objectives. Overcoming communication barriers and predispositions about data privacy is often the biggest obstacle.
First, you need to design collaborative structures that add value to various parts of the ecosystem. It's about getting people around the same table, where they can see themselves being a part of this.
Understanding and explaining the use and benefits of these technologies takes time. Be prepared to invest in both learning and communication.
Companies are at different stages regarding the solutions they need. Assess your current level and tailor your approach accordingly.
Communication and education are the primary challenges, as the technologies are already available and ready to use.
People often have preconceived notions about what is possible and what is not. We are effectively opening new doors in this area, so it's essential for people to understand what we're doing and recognize the advantages. They must be prepared for a blend of revolutionary change and innovation.
We also covered the following, very interesting and important questions for the audience:
We are addressing privacy and AI, first of all it’s about creating a provable track of what is actually being done, what kind of data goes into the training, what is the actual revolving model and how is that being used. When we are using the model, we’re kind of fixing the model. We know exactly what the weights are in this case and we put that to the millions of users. Here you have most of the regulatory requirements and these are addressed with MPC which allows us to protect both the data that goes in but also the algorithms that have been trained.
A mixture of it all, time is a really critical factor, we saw under the pandemic, where there was a queue line that affected the waiting time for approval with up to one year, by the authorities despite the great projects and you have the amount of time before the data will become available on a secure platform for analytics, that was also over half a year, and lastly the structure of your data, that can also easily go on for a few years, so we are now up around two years, so the time frame is really critical here, because you can actually get answers on questions really really fast. And also the economy plays a role here, but I would say that time is the most important factor in this.
Blockchain especially is a really cool marketing term, so it's very easy to understand what a blockchain is, whereas MPC is more complicated, but blockchain and MPC actually go together. They are opposite technologies that solve two very different things and combined they actually solve the problem of the data economy that we have looked into today. The blockchain is what lies underneath our platform.
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