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S. Mazza:
"Homomorphic-Encrypted Volume Rendering";
Supervisor: I. Viola; Visual Computing and Human-Centered Technology, 2021; final examination: 2021-05-07.

Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct volume rendering approach that performs volume rendering directly on encrypted volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the volume data and rendered image are uninterpretable to the rendering server. Our volume rendering pipeline introduces novel approaches for encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party. Furthermore, we developed a big-integer (multiple-precision, or multiple word integer) library for Vulkan graphics pipeline. It facilitates the rendering of securely encrypted data on the GPU. It supports the calculation of common mathematical operations like addition, subtraction, multiplication, division. Moreover, it supports specialized operations for asymmetric cryptography like modular exponentiation with Montgomery reduction. We also introduce a testing framework for Vulkan that allows the automated testing of big-integer computations on the GPU.

http://dx.doi.org/10.34726/hss.2021.81045

https://publik.tuwien.ac.at/files/publik_300524.pdf