Computer Vision Security Researcher

4 weken geleden


Den Haag, Zuid-Holland, Nederland TNO Voltijd

About this position

Federated Learning (FL) is a technique that enables AI models to be trained with data from multiple parties without uploading the data to a central location. However, research has shown that some of the original data can be reconstructed from the model itself, which is done via so-called gradient inversion attacks. TNO is looking for a researcher to explore the vulnerabilities of current state-of-the-art algorithms for FL in terms of inversion attacks.

What will be your role?

Your role will be to enhance privacy by exploring vulnerabilities related to inversion attacks and mitigating these vulnerabilities when possible. You will start with a literature study to understand the current state-of-the-art of inversion attacks approaches on FL systems. You will also investigate weaknesses in current implementations and look for solutions to improve protection of a FL system against inversion attacks.

What we expect from you

We are looking for a student with a strong background in AI, data security, and advanced machine learning techniques. You should be interested in taking a deep dive into the world of gradient inversion attacks and helping us uncover weaknesses in state-of-the-art AI algorithms. You should also have some experience in computer vision, artificial intelligence, deep learning, and Python programming.

What you'll get in return

TNO offers a highly professional and innovative internship environment within a team of top experts. You will be properly supervised during your work placement and given the scope to get the best out of yourself. You will also receive a suitable internship allowance, possibility of eight hours of free leave per internship month, and a free membership of TNO's employee association.



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