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.

翻译：不同隐私(DP)是维护数据库中隐私敏感信息的偏离标准(DP),然而,在图像数据库中缺乏清晰和令人信服的DP背景化,在进行DP时,个人图像对某项分析的不可区分的贡献可以实现并观察。因此,由于整合DP而带来的隐私-准确性交换在差异私人图像数据库中表现得不够充分。这项工作的目的是通过清晰和直观的演示,将深层次差异图像与图像整合起来,使DP在图像数据库中的背景化。为此,我们设计了一种轻量级的DP方法,专门将图像数据库作为一个整体私有化,将图像数据库的统计精度保留到可调整的水平,同时将个人图像数据库的统计精度维持到可调整的水平,同时使个人图像对此类统计数据的可区分性做出贡献。因此,设计的方法利用了原则部分分析(PCA)来减少原始图像的属性,将大量特性转化为执行DP的较低维度,从而减少了DP在计算深层次的隐私与图像中的深度差异。为此,我们设计了将图像图像数据库的精度数据库私有化数据库的精度数据私有化化化,在内部分析中无法看到,我们通过机构化的系统化的系统化分析,在内部的系统化分析中可以通过内部的系统化分析来量化地分析,从而在内部分析中以可量化地分析,在内部的系统化的系统化的系统化的系统化的系统化的系统化分析中可以显示。