GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

Yu, Jinao; Xue, Hanyu; Liu, Bo; Wang, Yu; Zhu, Shibing; Ding, Ming

doi:10.3390/s21010058

Cited by 46 publications

(16 citation statements)

References 48 publications

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“…Another limitation of our study is that we consider the uniqueness of individuals' records as the exposure risk measure. As introduced in Jordan (2019), we plan to apply the differential privacy algorithm to the extent of a pre-determined threshold for disclosure risk and data post-process [29,30].…”

Section: Discussionmentioning

confidence: 99%

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

Park

Yoo

2021

Applied Sciences

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The financial sector accumulates a massive amount of consumer data that contain the most sensitive information daily. These data are strictly limited outside the financial institutions, sometimes even within the same organization, for various reasons such as privacy laws or asset management policy. Financial data has never been more valuable, especially when assessed jointly with data from different industries, including healthcare, insurance, credit bureau, and research institutions. Therefore, it is critical to generate synthetic datasets that retain the statistical or latent properties of the real datasets as well as the privacy protection guaranteed. In this paper, we apply Generative Adversarial Nets (GANs) to generating synthetic consumer credit data to be used for various educational purposes, specifically in developing machine learning models. GAN is preferable to other pseudonymization methods such as masking, swapping, shuffling, or perturbation, for it does not suffer from adding more attributes or data. This study is significant because it is the first attempt to generate the synthetic data of real-world credit data in practical use. The results find that synthetic consumer credit data using GAN shows a substantial utility without severely compromising privacy and would be a useful resource for big data training programs.

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Section: Discussionmentioning

confidence: 99%

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

Park

Yoo

2021

Applied Sciences

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“…The principle of Differential Privacy (DP) [36] is adopted by many face de-identification technologies [37]. Reference [38] proposed the SDC-DP algorithm, a novel noise dynamic allocation algorithm based on differential privacy using the standard deviation circle radius, which effectively reduces the relative error and improves the accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance

et al. 2022

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In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.

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“…However, dynamic objects may not only cause occlusion both visually and pragmatically but also collide with privacy issues with identity-related information in many SVI applications, especially the most widely used image-based localization. Therefore, several studies have proposed to clean SVIs by blurring sensitive information [22], replacing completely anonymous pedestrians [23], replacing sensitive objects with synthetic content generated by generative adversarial networks (GAN) [24], or removing dynamic objects using image-inpainting techniques [25]. Among them, advanced image-inpainting methods have shown superiority in removing dynamic objects with high visual quality.…”

Section: Related Workmentioning

confidence: 99%

A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding

Hu¹,

Jia²,

Zhang³

et al. 2022

Preprint

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Global Positioning Systems (GPS) have played a crucial role in various navigation applications. Nevertheless, localizing the perfect destination within the last few meters remains an important but unresolved problem. Limited by the GPS positioning accuracy, navigation systems always show users a vicinity of a destination, but not its exact location. Street view images (SVI) in maps as an immersive media technology have served as an aid to provide the physical environment for human last-meters wayfinding. However, due to the large diversity of geographic context and acquisition conditions, the captured SVI always contains various distracting objects (e.g., pedestrians and vehicles), which will distract human visual attention from efficiently finding the destination in the last few meters. To address this problem, we highlight the importance of reducing visual distraction in image-based wayfinding by proposing a saliency-guided image inpainting framework. It aims at redirecting human visual attention from distracting objects to destination-related objects for more efficient and accurate wayfinding in the last meters. Specifically, a context-aware distracting object detection method driven by deep salient object detection has been designed to extract distracting objects from three semantic levels in SVI. Then we

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GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

Cited by 46 publications

References 48 publications

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance

A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding

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