PLCOM: Privacy-preserving outsourcing computation of Legendre circularly orthogonal moment over encrypted image data

Yang, Tengfei; Ma, Jianfeng; Miao, Yinbin; Liu, Ximeng; Wang, Xuan; Meng, Qian

doi:10.1016/j.ins.2019.07.078

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…In other words, the special optimization of moment-based image representation considering background knowledge is quite necessary in practice. Specifically, we noted a number of potential efforts [74,76,92,118,173,215,216,225,226].…”

Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

Qi,

Zhang,

Wang

et al. 2021

Preprint

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Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications towards understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This paper presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, and definition extension. We also create a software package for a variety of widely-used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting realworld applications.

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Section: Concluding Remarks and Future Directionsmentioning

confidence: 99%

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

Qi,

Zhang,

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Among varieties of techniques to protect privacy, e.g., anonymization [10], encryption [11], data access control [12], data outsourcing [13], digital forgetting [14], and data summarization [15], differential privacy (DP) offers a promising approach to make the contribution of individual data items hardly distinguishable toward given data analyzing tasks [16]. Such a feature leads to a provable privacy guarantee with a quantitative privacy measurement called privacy budget [17], making DP the de-facto standard for privacy preservation in data analysis both in academia and industry [18,19].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Ma²,

Cao³

et al. 2022

Preprint

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Differential privacy (DP) has been the de-facto standard to preserve privacy-sensitive information in databases. Nevertheless, there lacks a clear and convincing contextualization of DP in the image database, where individual images' indistinguishable contribution to a specific 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 differentiallyprivate image databases. This work aims at contextualizing DP in an image database by an explicit and intuitive demonstration of integrating conceptional differential privacy with images. To this end, we design a lightweight approach dedicated to privatizing the 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 principal component analysis (PCA) to reduce the raw image with a large number of attributes to a lower-dimensional space, whereby DP is performed 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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Secure robot face recognition in cloud environments

Chiranjeevi

2021

Multimed Tools Appl

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PLCOM: Privacy-preserving outsourcing computation of Legendre circularly orthogonal moment over encrypted image data

Cited by 6 publications

References 25 publications

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

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

Secure robot face recognition in cloud environments

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