Privacy Preserving Image Registration
Riccardo Taiello, Melek \"Onen, Francesco Capano, Olivier Humbert and, Marco Lorenzi

TL;DR
This paper introduces a privacy-preserving framework for medical image registration that leverages cryptographic tools like secure multi-party computation and homomorphic encryption to enable secure, accurate, and scalable image alignment.
Contribution
It extends classical image registration methods with cryptographic techniques, optimizing performance for high-dimensional data and demonstrating feasibility in medical applications.
Findings
Privacy preserving registration is feasible with cryptographic tools.
The framework maintains accuracy comparable to standard methods.
Optimizations improve scalability and efficiency in high-dimensional scenarios.
Abstract
Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content.In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools,…
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Taxonomy
TopicsCryptography and Data Security · Complexity and Algorithms in Graphs · Chaos-based Image/Signal Encryption
