Comments on “PassBio: Privacy-Preserving User-Centric Biometric Authentication”

Kwon, Hee-Yong; Lee, Mun-Kyu

doi:10.1109/tifs.2022.3195380

Cited by 6 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their method does not reveal the actual distance, but only indicates whether the distance is within a predefined threshold. Kwon and Lee [31] commented on two vulnerabilities of Passbio and proposed a countermeasure against these vulnerabilities. Lee et al [15] proposed an efficient biometric authentication method specialized for Hamming distance.…”

Section: Related Workmentioning

confidence: 99%

Efficient Lp Distance Computation Using Function-Hiding Inner Product Encryption for Privacy-Preserving Anomaly Detection

Ryu

Jeon

Hong

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

In Internet of Things (IoT) systems in which a large number of IoT devices are connected to each other and to third-party servers, it is crucial to verify whether each device operates appropriately. Although anomaly detection can help with this verification, individual devices cannot afford this process because of resource constraints. Therefore, it is reasonable to outsource anomaly detection to servers; however, sharing device state information with outside servers may raise privacy concerns. In this paper, we propose a method to compute the Lp distance privately for even p>2 using inner product functional encryption and we use this method to compute an advanced metric, namely p-powered error, for anomaly detection in a privacy-preserving manner. We demonstrate implementations on both a desktop computer and Raspberry Pi device to confirm the feasibility of our method. The experimental results demonstrate that the proposed method is sufficiently efficient for use in real-world IoT devices. Finally, we suggest two possible applications of the proposed computation method for Lp distance for privacy-preserving anomaly detection, namely smart building management and remote device diagnosis.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient Lp Distance Computation Using Function-Hiding Inner Product Encryption for Privacy-Preserving Anomaly Detection

Ryu

Jeon

Hong

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

PalmSecMatch: A data-centric template protection method for palmprint recognition

Liu,

Shao,

Zhong

2024

Displays

View full text Add to dashboard Cite

A Privacy-Preserving Remote Heart Rate Abnormality Monitoring System

Zhang,

Yang

2023

IEEE Access

View full text Add to dashboard Cite

Heart rate arrhythmia is an important manifestation of common clinical cardiovascular diseases, posing a serious threat to human life and health. Due to its suddenness, insidiousness and rapid changes, it often causes patients to miss the best treatment time. Therefore, real-time monitoring of heart rate changes is particularly important to monitor and prevent the onset of such diseases. Nonetheless, heart rate information in telemedicine systems is often presented in plaintext, rendering it vulnerable to interception or tampering, and posing a substantial threat to users' privacy. To address this issue, we propose a privacypreserving remote heart rate abnormality monitoring system, which utilizes a privacy comparison protocol. By implementing a two-server model, the privacy comparison protocol ensures privacy not only during the comparison process but also in the resulting outcomes. During the monitoring process, the monitor is only able to obtain the final number of abnormalities of the patient's heart rate and cannot obtain information about the patient's original heart rate. This allows for a more rational and effective use of medical experts, so that patients can enjoy a high level and quality of service from medical experts without having to leave home. Finally, a detailed security analysis demonstrates that our scheme can effectively protect the privacy and security of patient medical data and hospital health indicators. And our experimental results show that our scheme is computationally efficient and the scheme is effective and feasible.

show abstract

Comments on “PassBio: Privacy-Preserving User-Centric Biometric Authentication”

Abstract: In 2018, Zhou et al. proposed PassBio, a privacy-preserving biometric authentication scheme, in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECU-RITY (TIFS)

Cited by 6 publications

References 4 publications

Efficient Lp Distance Computation Using Function-Hiding Inner Product Encryption for Privacy-Preserving Anomaly Detection

Efficient Lp Distance Computation Using Function-Hiding Inner Product Encryption for Privacy-Preserving Anomaly Detection

PalmSecMatch: A data-centric template protection method for palmprint recognition

A Privacy-Preserving Remote Heart Rate Abnormality Monitoring System

Contact Info

Product

Resources

About