Expression Preserved Face Privacy Protection Based on Multi-mode Discriminant Analysis

Wang, Xiang; Xiong, Chen; Pei, Qingqi; Qu, Youyang

doi:10.32604/cmc.2018.03675

Cited by 23 publications

(6 citation statements)

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“…The importance of this work is in the introduction of the notion of controllable privacy, where a user can specify, which attributes to conceal and which to preserve, something that is important, for example, when sharing images online and across social media. Similar work using MMDA for deidentification was also presented in [193].…”

Section: Statistical Modelsmentioning

confidence: 74%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

108

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Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy-related research in the area of biometrics and review existing work on Biometric Privacy-Enhancing Techniques (B-PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B-PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

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Section: Statistical Modelsmentioning

confidence: 74%

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Meden

Rot

Terhörst

et al. 2021

IEEE Trans.Inform.Forensic Secur.

108

View full text Add to dashboard Cite

show abstract

“…Huang et al [Huang, Du and Chen (2005)] further proposed two other data reconstruction methods: PCA-DR and MLE-DR. In addition, several distribution reconstruction algorithms have been proposed in correspondence to different randomization operators [Agrawal and Aggarwal (2001); Evfimievski, Srikant, Agrawal et al (2002); ; Wang, Wang, Guo et al (2018); Wang, Xiong, Pei et al (2018)]. The basic idea of most algorithms is to use Bayesian analysis to estimate the original data distribution based on the randomization operator and the randomized data.…”

Section: Methods Of Randomization For Privacy Preserving On Original Datamentioning

confidence: 99%

Research on Privacy Preserving Data Mining

Wang¹,

Chen²,

Wang³

2019

Journal of Information Hiding and Privacy Protection

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In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation. The existing problems and directions for future research are also discussed.

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“…In Powers et al [Powers, Alling, Osolinsky et al (2015)], a mobilecloudlet-cloud architecture was proposed to perform real-time face recognition by using cloudlet to pre-process data and reduce communication time at the expense of computation. Wang et al [Wang, Xiong, Pei et al (2018)] introduces a method to protect the visual privacy by hiding the identity information of the face images. MOCHA [Soyata, Muraleedharan, Funai et al (2012)] is a mobile-cloudlet-cloud architecture for real-time face recognition that performs task migration from mobile devices to the cloud and dynamically distributes computational load between the cloud and cloud servers.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Face Recognition System Based on Azure IoT Edg

Shen¹,

Liu²,

Liang³

et al. 2019

Computers, Materials &Amp; Continua

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With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture "client-cloud" face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and efficient edge computing accelerated architecture. By offloading part of the computing tasks to the edge server closer to the data source, edge computing resources are used for image preprocessing to reduce the number of images to be transmitted, thus reducing the network transmission overhead. Moreover, the application code does not need to be rewritten and can be easily migrated to the edge server. We evaluate our schemes based on the open source Azure IoT Edge, and the experimental results show that the three-tier architecture "Client-Edge-Cloud" face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.

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Expression Preserved Face Privacy Protection Based on Multi-mode Discriminant Analysis

Cited by 23 publications

References 0 publications

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Research on Privacy Preserving Data Mining

Optimization of Face Recognition System Based on Azure IoT Edg

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