Anonymization

Data privacy is a complex challenge for everyone—users, companies, and even governments. That’s the case with Strava’s heat maps, which resurfaced recently after Le Monde reported that the app inadvertently exposed sensitive information about world leaders.

It seems that it is impossible to benefit from AI advantages while keeping patient data intact and safe. To overcome this, several AI companies have found the answer to many problems in synthetic data: data sharing, algorithm training, and collaboration with third parties.

Sharing data for the purposes of data analysis can have huge benefits. The question is how to do so in a way that protects privacy.

Privacy can limit the amount of available data in healthcare. Sometimes, it seems like it is not possible to benefit from the AI advantages while keeping intact and safe patients’ data.

Analytics and BI are essential for business. But what if you handle sensitive health data? Here, we explain what you need for health data BI and analytics.

Pseudonymization is a sensible security precaution but it will not make your application GDPR compliant by itself. In this blog we look in more detail at pseudonymization and the implications of using it.

GDPR fines are starting to bite. Find out about 3 of the most recent rulings from across Europe. Then read the 7 most important lessons the fines teach us.

By storing data locally on a user's device, you have fewer administrative obligations compared to when you transfer and process data in the cloud. However, you still need to provide methods to delete data in case of device theft or loss.

As a Digital Health Enterprise, one of your first concern should be how to protect the health sensitive data that you are collecting from your users and storing/managing in your service.