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.
翻译:计算要求很高的任务通常由专门的数据中心计算,实时视觉化也遵循这一趋势。但是,有些任务需要最高程度的保密性,这样除了所有者之外,任何其他方都无法读取或查看敏感数据。这里我们展示了直接的量化转换方法,通过使用同质 Paillier 加密算法,对加密量数据进行量化直接转换;这种方法确保了数量数据和成像无法被传输服务器解释。我们的数据传输管道为加密数据配置、内插和不透明调控以及简单的传输功能设计引入了新的方法,而其中每个常规都保持最高程度的隐私。我们展示了与我们隐私保护计划相关的性能和存储管理器分析。我们的方法通过设计而开放和安全地直接生成加密的量数据,而不是通过模糊性能加密。数据的所有者只需保持其安全的关键保密性,才能保证其数量数据和成像的隐私。我们的工作是,根据我们的知识,第一个保存隐私的远程量变换功能设计,以及简单的传输功能设计,其中每个常规都保持最高程度的隐私和高度的隐私和高度的隐私和记忆性间接性分析,而不需要任何敏感服务器的泄漏。