A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks

Ma, Yukun; Wu, Lifang; Gu, X.; He, Jiaoyu; Yang, Zhou

doi:10.1109/access.2017.2737544

Cited by 48 publications

(26 citation statements)

References 9 publications

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“…Ma et al [246] described a PHE encrypted face verification system, where the main idea is to extract facial features using deep neural networks and then encrypt the computed features with the Paillier cryptosystem [258]. To calculate distances during verification (between two encrypted vectors of facial features), a Hamming distance is used.…”

Section: Homomorphic Encryption Techniquesmentioning

confidence: 99%

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: Homomorphic Encryption Techniquesmentioning

confidence: 99%

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

“…Paillier cryptosystem is used in designing the secure model which can guarantee the result of the system keep same as that in the standard Eigenfaces algorithm. Reference [13] also proposed a secure face-verification system, deep neural networks is used to extract the face features and two servers are involved in this system, a data server stores the encrypted face features of the user and a verification server is used for performing face verification. Paillier encryption is used for protecting the face features and all data is transmitted in ciphertext, so no parties can decrypt it except for the verification server.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Edge Computation-Based Face Verification System for User Authentication

Wang

Xue

et al. 2019

IEEE Access

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The face recognition has become a common means of identity authentication because of the advantages of uniqueness, non-invasive and not easy to be stolen. The outsourcing of face recognition to the service provider is a typical manner nowadays. However, it raises vital concerns about the privacy of outsourcing server due to the sensitivity of face data. Therefore, a frame of identity authentication based on the technology of privacy-preserving face recognition is presented in this paper. The convolutional neural network is used for face feature extraction. To overcome the issue of privacy leaked, a method of secure nearest neighbor that can compute the cosine similarity over encrypted feature vectors is proposed. What's more, the edge computing is introduced in our frame to increase the authentication efficiency by removing some operations from the cloud to the edge of the Internet. Moreover, we also propose a secret sharing homomorphism technology which is used for distributed computing to improve the fault-tolerance of our identity authentication system. The experimental results show that the proposed schemes are secure and effective. INDEX TERMS Face recognition, edge computing, privacy protection.

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“…In [21] proposed a secure face verification scheme using a specifically trained neural net. They extracted the features from the last layer of the network.…”

Section: Related Workmentioning

confidence: 99%

Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System

2018

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Biometric verification systems have become prevalent in the modern world with the wide usage of smartphones. These systems heavily rely on storing the sensitive biometric data on the cloud. Due to the fact that biometric data like fingerprint and iris cannot be changed, storing them on the cloud creates vulnerability and can potentially have catastrophic consequences if these data are leaked. In the recent years, in order to preserve the privacy of the users, homomorphic encryption has been used to enable computation on the encrypted data and to eliminate the need for decryption. This work presents DeepZeroID: a privacy-preserving cloud-based and multiple-party biometric verification system that uses homomorphic encryption. Via transfer learning, training on sensitive biometric data is eliminated and one pre-trained deep neural network is used as feature extractor. By developing an exhaustive search algorithm, this feature extractor is applied on the tasks of biometric verification and liveness detection. By eliminating the need for training on and decrypting the sensitive biometric data, this system preserves privacy, requires zero knowledge of the sensitive data distribution, and is highly scalable. Our experimental results show that DeepZeroID can deliver 95.47% F1 score in the verification of combined iris and fingerprint feature vectors with zero true positives and with a 100% accuracy in liveness detection.

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A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks

Cited by 48 publications

References 9 publications

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

A Privacy-Preserving Edge Computation-Based Face Verification System for User Authentication

Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System

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