Graduation project: differential privacy for medical research

Graduation project: differential privacy for medical research
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Graduation project: differential privacy for medical research

  • Location Groenlo
  • Business unit Healthcare

Opening up all medical records would be a huge enabler for science; for both individual studies and the amount of researchers with access to data. But medical data naturally needs to be kept private.

Differential Privacy [1] is a robust framework to tackle this problem in the area of statistical disclosure control in a manner that automatically adapts to the query and available data. We'd like to bring this powerful framework into practice and help make medical data more accessible for medical researchers while at the same time giving stronger privacy protections than what's currently possible in data-sharing agreements.

The platform developed by Nedap Healthcare supports the daily work of many healthcare workers dealing with millions of patient-records in the Netherlands. Our datasets contain everything from a medical diagnosis, prescribed medication, allergies up until the bill that is sent to the insurer and everything in between.

 

Differential privacy has become more accessible with the release of open-source implementations [3], but there are still gaps with the medical research practice that we'd like to close. We're looking for a bright studentwho is interested in bridging this gap. You will study the fundamental mathematical operations of differential privacy. Analyse the impact of generated noise in a dataset and its effect on the validity of medical statistical tests. Some of our key questions are:

  • To what extent can medical statistical queries be executed in the context of differential privacy?
  • How does the adaptive (Gaussian/Laplacian) noise translate to the probabilities obtained via a statistical test (e.g. t-test, chi^2-test)?
  • Which DP methods scale to larger datasets and what are important aspects to consider in terms of data structures, pre-processing and data quality?
  • What is the proper epsilon (anonymity)-tradeoff for various queries in the medical domain? How can we assist users in this trade-off in an intuitive manner? [2]

Key references:

  1. Dwork, Cynthia. "Differential privacy: A survey of results." International conference on theory and applications of models of computation. Springer, Berlin, Heidelberg, 2008.
  2. Dwork, Cynthia, Nitin Kohli, and Deirdre Mulligan. "Differential privacy in practice: Expose your epsilons!." Journal of Privacy and Confidentiality 2019.
  3. SmartNoise - https://github.com/opendp/smartnoise-samples/blob/master/data/README.md

Your team

You'll primarily work in the Tech Exploration Team within Nedap Healthcare whose mission is to find and apply new technology to solve problems in the Healthcare sector. You'll also work closely with the Data Science team and (technical) physicians in order to get rapid feedback about your work.

Our offer

You can work from home or at our cool campus in Groenlo, the choice is yours. You will of course receive a laptop & monthly internship allowance and are welcome to attend the 'Nedap Student Day' which is organized every six months. This is an excellent way to get in touch with interns, graduates and working students from other market groups within Nedap.

Required experience and skills

We're looking for someone with a background in mathematics with an interest in the fundamentals of statistical analysis. Some knowledge about clinical research is a plus, but we've got in house experience to cover that part.

Nedap, Healthcare

About Nedap Healthcare

We help care givers and nurses to save time on administrative tasks. With our technology, they are able to spend less time on registering, planning, reporting, and drawing up care plans, allowing them to devote more time to their patients. The family can also be informed immediately about the patient’s wellbeing at the touch of a button.

Application procedure

Are you triggered by one or more of these questions and do you want to help improve the privacy of millions of patient-records? Let's get in touch! Contact Jaap Grondman (jaap.grondman@nedap.com) or apply immediately via the application button above.

     

    • 1. Your application
    • 2. First interview
    • 3. Next meet & greet
    • 4. Offer
    • 5. High five!
    Step 1
    Your application

    You’ve applied? Awesome! We will reply to your application as fast as possible, but at least within 10 workdays via e-mail.

    We respect your privacy, therefore you can only apply via our website. Applications via e-mail will not be accepted.

    Questions? Get in touch with our recruiter