Face-Image Anonymization as an Application of Multidimensional Data k-anonymizer

Nakamura, Taichi; Sakuma, Yuiko; Nishi, Hiroaki

doi:10.15803/ijnc.11.1_102

Cited by 7 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Facial anonymization is a common practice for preserving personal privacy. Recently, generative adversarial network-(GAN-) based deep learning (DL) models have been widely used for face swapping and anonymization [31][32][33][34]. In our previous study [31], we demonstrated a robust approach to preserving the facial identity of the occupants in a FAV cabin.…”

Section: Facial Privacy Versus Facial Recognition In Monitoringmentioning

confidence: 99%

“…Anonymization of faces is an easier and more robust solution to personal privacyrelated threats in the digital domain [35]. Blurring, masking faces, or creating a patch over faces is slightly easier than any other face anonymization approach; however, those methods suffer from significant loss of facial information [32,36]. erefore, face swapping has attracted significant attention for facial anonymization purposes.…”

Section: Face Anonymizationmentioning

confidence: 99%

See 1 more Smart Citation

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Cha

Kim

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Fully autonomous vehicles (FAVs) lack monitoring inside the cabin. Therefore, an in-cabin monitoring system (IMS) is required for surveilling people causing irregular or abnormal situations. However, monitoring in the public domain allows disclosure of an individual’s face, which goes against privacy preservation. Furthermore, there is a contrary demand for privacy in the IMS of AVs. Therefore, an intelligent IMS must simultaneously satisfy the contrary requirements of personal privacy protection and person identification during abnormal situations. In this study, we proposed a privacy-preserved IMS, which can reidentify anonymized virtual individual faces in an abnormal situation. This IMS includes a step for extracting facial features, which is accomplished by the edge device (onboard unit) of the AV. This device anonymizes an individual’s facial identity before transmitting the video frames to a data server. We created different abnormal scenarios in the vehicle cabin. Further, we reidentified the involved person by using the anonymized virtual face and the reserved feature vectors extracted from the suspected individual. Overall, the proposed approach preserves personal privacy while maintaining security in surveillance systems, such as for in-cabin monitoring of FAVs.

show abstract

Section: Facial Privacy Versus Facial Recognition In Monitoringmentioning

confidence: 99%

Section: Face Anonymizationmentioning

confidence: 99%

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Cha

Kim

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…sensitivity(l) = ||max( p i l ) − min( p j l )|| 1 , i and j ∈ {1, 2, ..., n}, i = j (10) where p i l and p j l mean the value for the l-th attribute in the i-th and j-th reduced image data, respectively, and sensitivity(l) describes the range that the value of the l-th attribute can differ in the database. With the distribution of the noise n l settled, noise can be generated and injected to obtain the differentially private image database D r dp = {I dp 1 , I dp 2 , ..., I dp n }, where…”

Section: B Differential Privacy For the Dimension-reduced Image Datamentioning

confidence: 99%

“…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

View full text Add to dashboard Cite

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.

show abstract

“…GAN-Encoders aims to find out w corresponding to the input X In , and w itself also has semantic sense [6]. It is meaningful in image manipulation [7,8,9], compression, restoration, editing and enhancement tasks.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

Expressive And Compressive Gan Inversion Network

Xu¹,

Zhang²

2021

2021 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

This paper presents a novel encoding framework for the generative adversarial network (GAN), named compressive GAN inverter (Comp-GI), which can effectively compress, restore, and edit the attributes of real-world images. In particular, Comp-GI first transforms the input image into the latent variable space of GAN through an encoder network and then manipulates the latent code vector to enable various semantic image editing operations. StyleGAN [1] and its improved version StyleGANv2 [2] are adopted as the default GANs for image generation. Experiments on benchmark datasets FFHQ demonstrate that the proposed Comp-GI achieves better restoration performance with finer details and fewer artifacts than existing state-of-the-art GAN-inversion methods.

show abstract

Face-Image Anonymization as an Application of Multidimensional Data k-anonymizer

Cited by 7 publications

References 6 publications

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

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

Expressive And Compressive Gan Inversion Network

Contact Info

Product

Resources

About