Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms

Ghazarian, Arin; Zheng, Jianmin; El-Askary, H. M.; Fu, Guohua; Rakovski, Cyril

doi:10.1109/embc46164.2021.9630880

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…In general, our findings align with similar research on state-of-the-art non-wearable devices. For example, 12-lead ECG data from two open access databases combined with other electronic health records data from 40,000 patients revealed 15 CIR rates in similar ranges to those reported from studies using wearable ECGs. The researchers looked at 37 heart conditions, including supraventricular tachycardia, ST depression, and pacing rhythm, and recorded an overall CIR of 94·56%.…”

Section: Discussionsupporting

confidence: 65%

“…Any such data may be used to re-identify an individual. 15 This paper explores open questions surrounding re-identification through an extensive systematic review of available literature. For example, what types of wearable data, how much of that data, and what resolution of such data can enable re-identification are all critical questions that remain unanswered.…”

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confidence: 99%

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Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Chikwetu

Woldetensae

et al. 2022

Preprint

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It remains unknown whether de-identifying wearable biometric monitoring data is sufficient to protect the privacy of individuals in the dataset. This systematic review seeks to shed light on this. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on December 6, 2021 (PROSPERO CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Though our search strategy had no language restrictions, all retrieved studies were in English. We included studies demonstrating re-identification, identification, or authentication using data from wearables. Our search returned 17,625 studies, and 72 studies met our inclusion criteria. Our findings demonstrate that substantial re-identification risk exists in data from sensors generally not thought to generate identifiable information, such as the electrocardiogram and electromyogram. In many cases, only a small amount of data (1-300 seconds of recording) is sufficient for re-identification.

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Section: Discussionsupporting

confidence: 65%

mentioning

confidence: 99%

Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Chikwetu

Woldetensae

et al. 2022

Preprint

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“…However, Becerra et al [ 78 ] stated that even though cardiac conditions affect the performance of the system, accuracies can be higher for some classifiers. Moreover, Ghazarian et al [ 83 ] achieved different accuracies for different heart conditions, meaning that feature selection and classification optimization should be performed considering different cardiac conditions. Posture: Postures like standing or lying down differ widely on the position and shape of internal organs.…”

Section: Discussionmentioning

confidence: 99%

“…Contrarily, Chiu et al [ 82 ] registered a drop of 19% between identifying normal subjects and subjects with arrhythmia (100% and 81%, respectively). Moreover, Ghazarian et al [ 83 ] assessed the accuracy of ECG-based identification for distinct heart condition groups. They discovered that, in contrast to the initial expectation that identification accuracy for healthy normal sinus rhythm should be the highest, the identification accuracy is higher for patients with sinus tachycardia or patients who are diagnosed with both ST changes and supraventricular tachycardia.…”

Section: Ecg Acquisition and Databasesmentioning

confidence: 99%

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

Pereira

Conceição

Sencadas

et al. 2023

Sensors

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In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.

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“…In recent years, the International Conference on Computer Vision and Pattern Recognition and other top conferences in the feld of computer vision will contain many excellent papers related to face recognition methods. Many research institutions promote the development of face recognition methods through research and communication [6][7][8]. To date, research on face recognition technology has gained rich experience, but it is still limited in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

Xue

Wang

2023

Computational Intelligence and Neuroscience

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In this research, a robust face recognition method based on adaptive image matching and a dictionary learning algorithm was proposed. A Fisher discriminant constraint was introduced into the dictionary learning algorithm program so that the dictionary had certain category discrimination ability. The purpose was to use this technology to reduce the influence of pollution, absence, and other factors on face recognition and improve the recognition rate. The optimization method was used to solve the loop iteration to obtain the expected specific dictionary, and the selected specific dictionary was used as the representation dictionary in adaptive sparse representation. In addition, if a specific dictionary was placed in a seed space of the original training data, the mapping matrix can be used to represent the mapping relationship between the specific dictionary and the original training sample, and the test sample could be corrected according to the mapping matrix to remove the contamination in the test sample. Moreover, the feature face method and dimension reduction method were used to process the specific dictionary and the corrected test sample, and the dimensions were reduced to 25, 50, 75, 100, 125, and 150, respectively. In this research, the recognition rate of the algorithm in 50 dimensions was lower than that of the discriminatory low-rank representation method (DLRR), and the recognition rate in other dimensions was the highest. The adaptive image matching classifier was used for classification and recognition. The experimental results showed that the proposed algorithm had a good recognition rate and good robustness against noise, pollution, and occlusion. Health condition prediction based on face recognition technology has the advantages of being noninvasive and convenient operation.

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Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms

Cited by 8 publications

References 16 publications

Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

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