Practical Privacy-Preserving Face Authentication for Smartphones Secure Against Malicious Clients

Im, Jong-Hyuk; Jeon, Seong-Yun; Lee, Mun-Kyu

doi:10.1109/tifs.2020.2969513

Cited by 43 publications

(39 citation statements)

References 46 publications

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“…In this way, the distortion parameters can be modified if a cancelable feature is compromised, producing a new template. This improves the security level by enabling the use of several disrupted templates, all of them associated with the same biometric information, but may reduce the performance of the authentication system [ 87 ]. Cancelable biometrics are usually divided into two categories [ 84 , 88 ]: Salting : An invertible transformation is used to alter the features, hence being possible to recuperate the original information; Non-invertible : The distortions are produced with an irreversible function, which imply better diversity and revocability than salting methods.…”

Section: Privacy-preserving Approachesmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

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Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

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Section: Privacy-preserving Approachesmentioning

confidence: 99%

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Hernández–Álvarez

Fuentes

González-Manzano

et al. 2020

Sensors

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“…Im et al [12] proposed a face-feature based authentication system for mobile phones. They use Yao's garbled circuit based secure-multi party computation to secure the biometric data.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching

Çatak¹,

Yayilgan²,

Abomhara³

2020

Preprint

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One of the most reliable methods of authentication used today is biometric matching. This authentication process, which is done by using biometrics information such as fingerprint, iris, face, etc. is used in many application areas. Authentication at border gates is one of these areas. However, some restrictions have been introduced to storing and using such data, especially with the General Data Protection Regulation (GDPR). The main goal of this work is to find the practical implementation of fully homomorphic encryption-based biometric matching in border controls. In this paper, we propose a biometric authentication system based on hash expansion and fully homomorphic encryption features, considering these restrictions. One of the most significant drawbacks of the homomorphic encryption method is the long execution time. We solved this problem by executing the matching algorithm in parallel manner. The proposed scheme is implemented as proof-of-concept in the SMILE, and its advantages in privacy preservation has been demonstrated.

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“…With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a tool for authenticating their owners. This technique is used not only to unlock devices but also permit users to run security-critical applications, such as financial services [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Although their protocol was very fast, it required a high communication cost for preprocessing. In 2020, Im et al proposed a secure face verification system that guarantees a real-time authentication performance on a smartphone [ 2 ]. They evaluated the performance of their system with two experiments; (1) an experiment involving 30 users in a real-world environment and (2) an experiment using the public face datasets CFP [ 51 ] and ORL [ 52 ].…”

Section: Introductionmentioning

confidence: 99%

“…They evaluated the performance of their system with two experiments; (1) an experiment involving 30 users in a real-world environment and (2) an experiment using the public face datasets CFP [ 51 ] and ORL [ 52 ]. In this paper, we consider the data presented in [ 2 ] as the criteria to evaluate the performance of the proposed method, since the result in [ 2 ] is one of the most up-to-date practical works in the literature of privacy-preserving biometric authentication. A more extensive survey of FE-based privacy-preserving applications can be found in [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

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Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

Jeon

Lee

2021

Sensors

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With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement—one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method.

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Practical Privacy-Preserving Face Authentication for Smartphones Secure Against Malicious Clients

Cited by 43 publications

References 46 publications

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review

A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching

Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification

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