Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

• URL: http://arxiv.org/abs/2202.08309v1
• Date: Wed, 16 Feb 2022 19:36:49 GMT
• Title: Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse
• Authors: Shiliang Zhang, Xuehui Ma, Hui Cao, Tengyuan Zhao, Yajie Yu, Zhuzhu Wang
• Abstract summary: Differential privacy (DP) has been the de-facto standard to preserve privacy-sensitive information in database. The privacy-accuracy trade-off due to integrating DP is insufficiently demonstrated in the context of differentially-private image database. This work aims at contextualizing DP in images by an explicit and intuitive demonstration of integrating conceptional differential privacy with images.
• Score: 35.06571163816982
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Differential privacy (DP) has been the de-facto standard to preserve privacy-sensitive information in database. Nevertheless, there lacks a clear and convincing contextualization of DP in image database, where individual images' indistinguishable contribution to a certain analysis can be achieved and observed when DP is exerted. As a result, the privacy-accuracy trade-off due to integrating DP is insufficiently demonstrated in the context of differentially-private image database. This work aims at contextualizing DP in image database by an explicit and intuitive demonstration of integrating conceptional differential privacy with images. To this end, we design a lightweight approach dedicating to privatizing image database as a whole and preserving the statistical semantics of the image database to an adjustable level, while making individual images' contribution to such statistics indistinguishable. The designed approach leverages principle component analysis (PCA) to reduce the raw image with large amount of attributes to a lower dimensional space whereby DP is performed, so as to decrease the DP load of calculating sensitivity attribute-by-attribute. The DP-exerted image data, which is not visible in its privatized format, is visualized through PCA inverse such that both a human and machine inspector can evaluate the privatization and quantify the privacy-accuracy trade-off in an analysis on the privatized image database. Using the devised approach, we demonstrate the contextualization of DP in images by two use cases based on deep learning models, where we show the indistinguishability of individual images induced by DP and the privatized images' retention of statistical semantics in deep learning tasks, which is elaborated by quantitative analyses on the privacy-accuracy trade-off under different privatization settings.

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