Secure Data Management, Privacy Preserving Data Exchange

Our team has a 10+ years of experience in designing various privacy preserving solutions allowing to securely apply computation on top of private data sets. Within our experience we have executed various projects in the financial & health industries.
Our team has also developed a proprietary Federated Learning protocol and crypto library allowing to execute any kind of computation on top of any type of any type of data in a privacy preserving way.

Problem

  • Accessing private protected data like healthcare is complicated, costly & time consuming due to the need of anonymizing & aggregating data is an untrusted environment.
  • Sometimes, like in the case of cross-border data exchange, it is impossible to move the data out of its premise and aggregate it due to regulations.
  • Data providers loose control and ownership of their data when sharing it to a third party in a raw way.
Closeup of programming code and language
Stock market charts

Solution

  • Our solution allows to securely perform computation directly on a premise of the data provider(s) and send weighted aggregated results to the data consumer.
  • The whole process becomes faster, cheaper 6 more secure for both parties.
  • The protocol developed by our team is shows the best results on the market in terms of time of operation & computation costs.

Examples

Analyzing / AI training on top of private datasets (e.g. healthcare records, financial or public data) without data leaving the owner´s premise. ​

Real-time analysis based on several private data bases (e.g KYC over several data sources)​

Anonymous auction & price bidding.

Data monetization without sharing the raw data itself.