Abstract
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.
Highlights
Volume rendering is extensively used in domains where the underlying data is considered highly confidential
If an amount of data in the range of the volume itself needs to be transferred from the server to the client, where the data would need to be encrypted and decrypted, it is pointless to perform any calculations on the server, because the client has more work to do than in a classical volume rendering on the client
While the expressiveness of our renderings is far from what is possible with state-of-the-art algorithms for non-encrypted data, we have presented a highly parallelizable direct volume rendering approach that allows the outsourcing of the storage of the volume data, and the outsourcing of the whole rendering pipeline, without compromising the privacy of the data
Summary
Volume rendering is extensively used in domains where the underlying data is considered highly confidential. When the clients that hold the secure key request rendering, the server performs raycasting directly on the encrypted volume data. This computation results in an image containing encrypted values, which is sent to the client. One future research goal should attempt to make a remote rendering system fast enough to achieve this This leads to our second requirement, which is to only use techniques that will not prevent the system from scaling the performance with the computational power available on the server and will not prohibit interactive frame rates. A privacy-preserving remote volume rendering can make the cloud more attractive as a storage space for volume data, because with our proposed technique, it is no longer necessary to download the whole dataset before images can be synthesized from it
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