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Obfuscated volume rendering
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AI
DOI：10.1007/s00371-015-1143-6.png)
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摘要
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Analyzing and processing various data types in a privacy-preserving perspective has been researched in many disciplines; however, such an issue draws very limited attention in the research field of scientific visualization. We wondered if it is possible to delegate the rendering of a volume data set to a remote server(s) while still being able to preserve its privacy to certain extent. This paper presents a block-based volume data transformation algorithm that obfuscates a volume data set so as to reduce the user's privacy concern when the volume data set is to be uploaded to a remote server. In addition, a privacy-aware transfer function adjustment is proposed so that not only the privacy is protected during the rendering process, but also the computational loading could be leveraged to the server side as much as possible. Experimental results show that the proposed method yields visually satisfactory results compared with a normal direct volume rendering approach. Moreover, the decrease of the rendering efficiency caused by the proposed method is still controlled within an acceptable range. A case study proves that the proposed approach can be adopted in practice. This work explores the possibility of rendering a volume data set through remote server(s) while the privacy of data is still maintained.
Keyword:
Obfuscation
Volume rendering
Privacy preserving processing
期刊
IF:
2.9
论文数:
4.6K
被引数:
6.5K
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