From PET to CET

What does this mean? Many PETs seem, based on their name, to have been developed to facilitate the exchange of personal data in contexts where organizations want to collaborate with data. For example, to find agreement ('is person X also known to you') or to perform analyses on shared data or each other's data. 

In reality, PETs help with a much broader challenge: PETs assist organizations in collaborating with data. However, there are many examples of data that is so sensitive or confidential that you do not want or cannot easily share it. Think of competitively sensitive information or classified data. This is where PETs come into play. 

Imagine that due to sensitivity and confidentiality, you do not want to share all underlying data, but you are willing to share general questions or insights. This does not necessarily have to involve personal data, but they are questions where those same techniques for matching, multi-party computation and federated learning can play a facilitating role. This opens up new possibilities for collaboration. 

So, in essence, these should not be referred to as PETs, but as 'CETs' – 'collaboration enhancing technologies.' Technologies that make it possible to provide other organizations with insights and knowledge about your data without those other organizations needing to see the actual data. If the data itself does not need to be shared, a significant legal and administrative sensitivity of collaboration is addressed. After all, you maintain control and oversight over the data's use. 

Organizations looking to implement PETs should not only seek applications where personal data must be shared. PETs are suitable for all situations where organizations want to collaborate and share insights but are hesitant to share data considered sensitive or confidential. Recognizing this broadens the possibilities and applications of PETs. Oops, CETs.