Obfuscated volume rendering

Abstract

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. Electronic supplementary materialThe online version of this article (

Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

“…However, such information when integrated with openly available attributes, patient identity can be at risk: using small DNA sequences from the Y chromosome, researchers at MIT were able to extract the genealogical information (surname, relatives) and religious background of fifty people from the 1000 Genomes Project [GMG*13]. The same rationale applies to use of shapes in the case of volume rendering [CY16]. In summary, low‐accuracy channels do not guarantee the preservation of privacy and appropriate risks should be assessed in the context of the externally available information about the individuals who are represented.…”

Homomorphic-Encrypted Volume Rendering

“…Chou and Yang [3] present a volume rendering approach that attempts to make it difficult for an unintended observer to make sense of the volume dataset that resides on a server. This is done by, on the client's side, subdividing the original data into equally sized blocks.…”

“…Since Paillier's cryptosystem does not carry over multiplication of two encrypted values to plaintext, it is classified as a partially homomorphic encryption (PHE) scheme. Paillier can securely encrypt many values (e.g., 512 3 voxels of a volume) from a small number space (e.g., 2 10 possible density values), because it is probabilistic, which means that during the encryption, the obfuscation can map a single plaintext value randomly to a large number of possible encrypted values. This makes a simple "probing" for finding out the number correspondence impossible.…”

“…A volume with 512 × 512 × 512 voxels and a resolution of 8 bits per voxel requires 512 3 • 8 bits/8 bits = 134, 217, 728 Bytes = 128 MB. The same volume encrypted by Paillier HE with a pub- lic key length that can be considered as secure (2048 bits) requires 512 3 • 2 • 2048bits/8bits = 64 GB. If the scalar voxel values x i are replaced by the vectors v i with a length of 256, the volume will require 64 GB * 256 = 16TB.…”

Privacy preserving event sequence data visualization using a Sankey diagram-like representation

“…Dasgupta et al [2014] suggested future opportunities and challenges on privacy preserving visualization specifically for the analysis of electronic medical records (EMRs) data. Chou and Yang [2015] and Mohanty et al [2013] enabled the process of volume rendering to be done while keeping the content of the volume dataset to be obscured to unauthorized viewers. In information visualization literature, Dasgupta and Kosara [2011] applied the concepts of kanonymity [Sweeney 2002] and l-diversity [Machanavajjhala et al 2007] and proposed an adaptive visualization technique for privacy preservation in parallel coordinates.…”