Secure, distributed learning

Multi-Party Computing

This is a call to Join the running MPC use cases on Fraud Detection and Combatting Poverty, or join the ideation on Secure, distributed learning.

Within Techruption, we investigate applications of technologies for privacy-proof data analytics: Multi-Party Computation (MPC), and related techniques like federated learning, enable organizations to jointly analyze (and learn from) their sensitive data without having to share or reveal this data. See www.tno.nl/mpc for more information. Below more information on an upcoming use case on secure, distributed machine learning. There are also two running use cases (collaborative fraud detection and combatting poverty). For all three, also new partners are welcome!

Why?

Data is the new oil. It fuels your organization with new insights (opportunities, efficiency, …) after careful purification and analyses (e.g. artificial intelligence). There is even more to be gained through data collaborations with other organizations (e.g. complementary data), but there are many reasons for you to be unwilling or unable to share data. By secure distributed learning, we think of the application of several (fairly) novel techniques such as federated learning (FL) and secure multi-party computation (MPC) to collaboratively train or exploit e.g. machine learning models on distributed datasets without disclosing your raw data or other intermediate results.

What?

  • During this ideation, we intent to identify interesting opportunities/questions that concern secure, distributed learning. In contrast to the ‘regular’ use cases, we intend to start a more technically-oriented use case; that is, there will be less focus on one agreed (business) application and more focus on deepening knowledge and experience with these enabling technologies. Typical questions that you may think of are:
    • Which AI techniques can we already apply to distributed, sensitive data?
    • Which tools or platforms currently support such secure analyses and how do they compare?
    • What are the limitations and advantages of different approaches (e.g. FL versus MPC).
    • What are relevant applications of secure, distributed learning? What are the needs and requirements of involved organizations?

Potential deliverable of use case: white paper that presents outcome of (some of the above) use case questions.

Who?

Everyone is welcome to attend; however, a somewhat technical background and/or knowledge about the current status of your organization’s position regarding AI would help us to get the most out of this session.

 

Interested? Contact Pieter Custers for more info: pieter.custers@brightlands.com