# Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes

Authors: Seungil Noh; Jaehan Kim; Seokmin Lee; Youngshin Kang; Cheolsoo Park; Youngjoo Shin
Journal: Security and Communication Networks
 (2022)

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## Abstract

Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.


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## Body

## 1. Introduction

In order to identify and authenticate legitimate users, biometric authentication techniques using personal biometric information are being actively studied. In the past, authentication systems used various biometrics of individuals such as passwords or PINs as a means to identify users. Biometrics, such as fingerprint and face recognition, have received considerable attention as a new secure personal authentication method. Biometric-based authentication uses the unique physiological characteristics of individuals. A user’s personal biometric information, which can be obtained in a relatively simple way, is a method of discriminating between users using unique physiological characteristics. Such characteristics can be automatically identified, allowing access to a restricted subject. By using biometrics, security can certainly be enhanced.However, as security threats for fingerprints and face recognition have also evolved, additional biometrics, such as an electrocardiogram (ECG) has recently emerged in research and applications. Specifically, there is a serious problem with using external physical data, such as a user’s fingerprint and face, as physical data are easily forged or falsified. Attackers can cause security threats to the authentication system by modifying or duplicating the legitimate user’s physical data [1–3]. Since the ECG-based authentication scheme is difficult to forge or falsify compared to the existing schemes, researchers are actively studying the ECG-based authentication scheme that uses personal heart rhythm to identify the users.An ECG is an electrical measurement of cardiac activity, which reflects the muscle contractions of the heart. Ordinary persons with no health problems generally have similar cardiac rhythms. However, it still varies depending on personal characteristics, such as age, gender, physical fitness, and heredity. Therefore, variations in the heartbeat help in the identification and authentication of a person. Unlike other biometrics, the ECG signal is sufficiently secure to be used in authentication owing to restrictions on the attacker’s capability to carry out cybersecurity attacks.In the past, ECG signal data were used only in hospitals and medical institutions, but with the development of technology, it is also widely used as an authentication scheme. In particular, ECG signal data are not only secure but also has the advantage of collecting a large amount of signal data at once and dividing it into a large number of small pieces of data. Researchers can identify users using only a small amount of divided ECG data, so ECG data can be used as a means of authentication. Recent ECG-based authentication schemes can be largely divided into two categories: [4, 5] signal-based authentication and domain-based authentication.Signal-based authentication is a method that uses raw data directly for identification. Domain-based authentication methods, also calledfiducial point-based methods, extract fiducial points from raw ECG signal data and use the extracted fiducial points for personal identification. Owing to their high accuracy, domain-based authentication methods are widely used in many applications. Unfortunately, however, fiducial features used in domain-based methods include not only signals essential for identification but also privacy-related data such as gender, age, and disease status.Designing ECG-based authentication without considering privacy issues could result in the serious leakage of private information. In order to protect an individual’s privacy, it is necessary to identify the fiducial points that can expose an individual’s private information such as gender, age, and disease. In this study, we classify personal sensitive information in ECG signals to demonstrate privacy leakage vulnerabilities that can occur in ECG-based authentication schemes and propose a secure authentication scheme that prevents leakage of private information. In this paper, we address privacy issues in ECG-based authentication schemes that use fiducial points as their features. Specifically, we conducted in-depth studies on privacy leakage problems in existing domain-based authentication methods.For this purpose, we investigated state-of-the-art ECG-based authentication schemes proposed in the literature. We collected a total of 36 fiducial points that are used in authentication schemes and built classification models using these fiducial points as features. The classification models were constructed using the MIT-BIH arrhythmia database (https://physionet.org/content/mitdb/1.0.0/), a public dataset that contains raw ECG data from 48 individual participants with additional personal information such as gender, age, and disease status. We evaluated our classification models in an experimental setting. The evaluation results reveal that for certain fiducial points, our model can classify private information with an accuracy of 98.2% for the gender and age categories and 87.9% for the disease status.To address the privacy problems we revealed, we propose a new privacy-preserving ECG-based authentication scheme. Through a feature analysis, we identified nine fiducial points that areprivacy insensitive; that is, they contain no additional information except for the information necessary for identification. We used these fiducial points as features to construct our ECG-based authentication scheme. The evaluation results show that the proposed method leaks no private information under our classification model while achieving high accuracy in identification.The contributions of this paper are as follows.(i)
We address potential privacy leakage problems in fiducial point-based ECG biometrics. Specifically, we show that given an ECG database, we can classify personal information, such as age, gender, and even disease status. Based on this, we extract fiducial points that are sensitive to personal privacy exposure and analyze the results.(ii)
In addition, we utilize secure fiducial points that are insensitive to personal privacy. We construct a personal authentication using the proposed fiducial points and prove safety from the perspective of personal privacy exposure.The remainder of this paper is organized as follows. In Section2, we present some background knowledge and related work regarding this study. In Section 3, we analyze the privacy leakage problems of fiducial-points-based ECG authentication schemes. In Section 4, we propose a new privacy-preserving ECG authentication method that provides security against privacy leakage analysis. In Section 6, we provide some concluding remarks.


## 2. ECG Biometrics

### 2.1. ECG Signal

ECG is primarily used in clinical cardiology, and it is a commonly used signaling data in the medical community. Recently, ECG has been studied in the field of biometric-based authentication schemes for individuals [6, 7]. The ECG reflects the electrical activity resulting from the heart muscle contractions. Experts attach sensors to the surface of a person’s arms, legs, and chest to measure cardiac signals through conductive electrodes. The medical community has introduced 12 ECG leads according to how they are measured. By looking at and analyzing a personal heartbeat from 12 positions, it is possible to accurately measure an individual’s heartbeat. However, depending on the location of the sensor, the shape of the output signal is different [8, 9]. Figure 1 shows the shapes of leads I, II, and III among 12 ECG leads. The process of obtaining an ECG is illustrated in Figure 1. In the stationary state with no current flow, the myocardial cells contract, and an electric current begins to flow. Figure 1 shows that the myocardium then returns to its original state. In this case, it is able to obtain power at a slower rate than depolarization. That is, the myocardium composed of cardiomyocytes generates a distinct ECG wave through contractions, which generates a low and wide wave by repolarization. A personal ECG signal is unique owing to the sequence of cardiac muscle activation and the direction of cardiac mass for each individual [10–12].(i)
P wave: This wave is generated by a contraction of the atrium. The atrium is composed of a thin myocardium that is secreted into the ventricle, and the amount of the contracted myocardium is small, resulting in a small wave.(ii)
QRS complexity: This describes the contraction of the ventricular muscle. The more the myocardium contracts, the higher the wave. As it takes time for the contraction to propagate, the width of the wave becomes wider.(iii)
T wave: This is a wave generated by repolarization of the ventricles, which rises slowly from the baseline.(iv)
RR interval: This is the interval measured between two successive heartbeats. Variations in the RR interval contain a large amount of important information as it greatly affects the determination of a person’s pulse.Figure 1
Formation of the electrocardiogram.Owing to their uniqueness and universality, ECGs have been gaining significant attention since the 2000s. Figure1 shows an example of an ECG that shows the cardiac cycles and the flow of electricity according to the depolarization of the heart. ECG can be measured in several ways: by attaching electrodes to the hands and feet, or by attaching them to the chest. It is common to use 12 leads when measuring ECG, especially for diagnostic purposes. For ECG-based authentication, it is sufficient to use only 11-channel ECG data from among the 12 leads. We need to identify legitimate users by using an individual’s ECG extracted through the sensors. For this, we have to consider two factors for the authentication: the noise source and the number of ECG data. Since the extracted ECG signal inevitably contains noise, we should preprocess the data and get rid of it as much as possible. Noise may be caused by various factors, including sensor issues, body movement, and the resolution of the measuring device [13]. Therefore, it is extremely challenging or infeasible to obtain noise-free data and should consider the process of noise-elimination before being used for authentication. When we use an electrocardiogram to identify a user, we should also consider the number of ECG features that will be used for authentication, as it directly affects the accuracy of classification.The ECG has many fiducial points, which are unique for each person. Table1 enumerates some fiducial points that can be obtained through the lead II.Table 1
Lead II ECG feature at a heart rate of 60 bpm for a healthy adult.
Fiducial point (feature)Normal valueNormal limitPR interval160 ms±40 msRR interval100 ms±20 msQTc interval400 ms±40 msQRS duration120 ms±20 msQT duration (men)420 ms±20 msQT duration (women)435 ms±35 msP amplitude150 mV±5 mVT amplitude30 mV±20 mVP wave axis80°—QRS wave axis−30° to +90°—T wave axis0° to +75°—


### 2.2. Related Work

In this section, we present some previous work related to the vulnerability of ECG-based authentication schemes and privacy-preserving authentication schemes.


#### 2.2.1. Vulnerability of ECG-Based Authentication Schemes

Simon et al. [14] presented successful attacks on biometric authentication schemes. More specifically, they spoofed ECG signals to the Nymi band, which is an ECG authentication application equipment used in practice. They also conducted mapping attacks by masquerading an authorized user by modifying the Nymi band system.Karimian et al. [15] presented an online and offline morphing attack using a single ECG beat as a template. Their attacks showed 90% accuracy for an online scenario with limited resources. While they addressed the risks of the existing ECG authentication schemes, this type of attack is not practical because multiple ECG channels are generally used to perform authentication.Lehmann and Buschek [16] proposed an ECG-based authentication scheme that can be used with a continuous electrocardiogram device. In their paper, they mentioned that certain private information, such as one’s health status and lifestyle, can be inferred from the ECG features. However, they did not present details on the privacy leakage problem of ECG-based authentication schemes.Bera et al. [17] proposed a robust security design for ECG-based authentication by combining ECG features with other biometric information. They claim that their design can successfully mitigate system and network attacks such as man-in-the-middle, replay, and privilege escalation attacks. However, they did not address the privacy leakage problems in ECG-based authentication schemes.Labati et al. [18] proposed an ECG-based authentication scheme that uses domain-based features. They found that the ECG data contained personal information about the subject and discussed the privacy leakage problem in ECG-based authentication. However, they did not present a detailed privacy leakage analysis of the existing authentication schemes.


#### 2.2.2. Privacy-Preserving Authentication Schemes

Kang et al. [19] used a CNN-based generative adversarial networks (GAN) model in an ECG-based authentication scheme to generate an artificial ECG signal similar to an existing ECG signal. ECG signals contain sensitive individual information such as heart diseases. By utilizing the GAN model of personal information including the victim’s health status, their authentication scheme successfully eliminates heart disease and achieves a high authentication accuracy of 99.86%.Huang et al. [20] proposed a lightweight ECG-based authentication scheme that effectively protects personal information. They acquired ECG signal datasets from an online community and recruited 30 subjects. They obtained ECG signals from the subjects’ physical activities such as running and applied various ECG datasets to their model. They recorded electrocardiograms from online data sets and subjects performing various physical activities. Their proposed authentication scheme achieves the technique of differential privacy depending on the sensitivity of the data queries, making it impossible for an attacker to infer the patient’s ECG information. The scheme achieves high accuracy by applying soft thresholds while protecting privacy.Yang and Wang [21] proposed an ECG-based authentication scheme that can protect users’ personal information by using a noninvertible transformation called the manipulatable haar transform (MHT). They demonstrated the robustness of their authentication scheme against a variety of attacks, including replay attacks and brute force attacks. Their scheme also emphasizes efficiency by demonstrating the runtime and template size of the authentication scheme on IoT devices. Their proposed scheme can provide secure intranode authentication for wireless body sensor network (WBSN) and protect the user’s sensitive health information and user identity included in the ECG signal.Bhutra et al. [22] proposed a privacy-protecting biometric authentication scheme applicable in healthcare systems. Their authentication scheme performs ECG-based authentication in a complete end-to-end authentication system in the IoT ecosystem. They applied privacy-preserving algorithms based on previous research by Yao [23]. Their authentication protocol can protect users’ private information by modifying the distributed set intersection protocol [24].In the previous work, most of the research was conducted under the assumption that all of the system entities such as sensors and gateways of a health care system are safe. However, Nasr-Esfahani et al. [25] proposed a secure authentication method against internal and external attacks under the assumption that all entities of the healthcare system are untrusted. Their scheme can protect the user’s identity and the patient’s location by overcoming the mechanisms of Gope and Hwang [26] and the limitations of Wachsmann et al. [27], which are past studies. Their authentication technique can effectively facilitate mutual authentication between major institutions while protecting patient privacy and location. Their proposed scheme shows that it is safe from various attacks (inside attacks, external attacks) while protecting the patient’s privacy and location.


## 2.1. ECG Signal

ECG is primarily used in clinical cardiology, and it is a commonly used signaling data in the medical community. Recently, ECG has been studied in the field of biometric-based authentication schemes for individuals [6, 7]. The ECG reflects the electrical activity resulting from the heart muscle contractions. Experts attach sensors to the surface of a person’s arms, legs, and chest to measure cardiac signals through conductive electrodes. The medical community has introduced 12 ECG leads according to how they are measured. By looking at and analyzing a personal heartbeat from 12 positions, it is possible to accurately measure an individual’s heartbeat. However, depending on the location of the sensor, the shape of the output signal is different [8, 9]. Figure 1 shows the shapes of leads I, II, and III among 12 ECG leads. The process of obtaining an ECG is illustrated in Figure 1. In the stationary state with no current flow, the myocardial cells contract, and an electric current begins to flow. Figure 1 shows that the myocardium then returns to its original state. In this case, it is able to obtain power at a slower rate than depolarization. That is, the myocardium composed of cardiomyocytes generates a distinct ECG wave through contractions, which generates a low and wide wave by repolarization. A personal ECG signal is unique owing to the sequence of cardiac muscle activation and the direction of cardiac mass for each individual [10–12].(i)
P wave: This wave is generated by a contraction of the atrium. The atrium is composed of a thin myocardium that is secreted into the ventricle, and the amount of the contracted myocardium is small, resulting in a small wave.(ii)
QRS complexity: This describes the contraction of the ventricular muscle. The more the myocardium contracts, the higher the wave. As it takes time for the contraction to propagate, the width of the wave becomes wider.(iii)
T wave: This is a wave generated by repolarization of the ventricles, which rises slowly from the baseline.(iv)
RR interval: This is the interval measured between two successive heartbeats. Variations in the RR interval contain a large amount of important information as it greatly affects the determination of a person’s pulse.Figure 1
Formation of the electrocardiogram.Owing to their uniqueness and universality, ECGs have been gaining significant attention since the 2000s. Figure1 shows an example of an ECG that shows the cardiac cycles and the flow of electricity according to the depolarization of the heart. ECG can be measured in several ways: by attaching electrodes to the hands and feet, or by attaching them to the chest. It is common to use 12 leads when measuring ECG, especially for diagnostic purposes. For ECG-based authentication, it is sufficient to use only 11-channel ECG data from among the 12 leads. We need to identify legitimate users by using an individual’s ECG extracted through the sensors. For this, we have to consider two factors for the authentication: the noise source and the number of ECG data. Since the extracted ECG signal inevitably contains noise, we should preprocess the data and get rid of it as much as possible. Noise may be caused by various factors, including sensor issues, body movement, and the resolution of the measuring device [13]. Therefore, it is extremely challenging or infeasible to obtain noise-free data and should consider the process of noise-elimination before being used for authentication. When we use an electrocardiogram to identify a user, we should also consider the number of ECG features that will be used for authentication, as it directly affects the accuracy of classification.The ECG has many fiducial points, which are unique for each person. Table1 enumerates some fiducial points that can be obtained through the lead II.Table 1
Lead II ECG feature at a heart rate of 60 bpm for a healthy adult.
Fiducial point (feature)Normal valueNormal limitPR interval160 ms±40 msRR interval100 ms±20 msQTc interval400 ms±40 msQRS duration120 ms±20 msQT duration (men)420 ms±20 msQT duration (women)435 ms±35 msP amplitude150 mV±5 mVT amplitude30 mV±20 mVP wave axis80°—QRS wave axis−30° to +90°—T wave axis0° to +75°—


## 2.2. Related Work

In this section, we present some previous work related to the vulnerability of ECG-based authentication schemes and privacy-preserving authentication schemes.


### 2.2.1. Vulnerability of ECG-Based Authentication Schemes

Simon et al. [14] presented successful attacks on biometric authentication schemes. More specifically, they spoofed ECG signals to the Nymi band, which is an ECG authentication application equipment used in practice. They also conducted mapping attacks by masquerading an authorized user by modifying the Nymi band system.Karimian et al. [15] presented an online and offline morphing attack using a single ECG beat as a template. Their attacks showed 90% accuracy for an online scenario with limited resources. While they addressed the risks of the existing ECG authentication schemes, this type of attack is not practical because multiple ECG channels are generally used to perform authentication.Lehmann and Buschek [16] proposed an ECG-based authentication scheme that can be used with a continuous electrocardiogram device. In their paper, they mentioned that certain private information, such as one’s health status and lifestyle, can be inferred from the ECG features. However, they did not present details on the privacy leakage problem of ECG-based authentication schemes.Bera et al. [17] proposed a robust security design for ECG-based authentication by combining ECG features with other biometric information. They claim that their design can successfully mitigate system and network attacks such as man-in-the-middle, replay, and privilege escalation attacks. However, they did not address the privacy leakage problems in ECG-based authentication schemes.Labati et al. [18] proposed an ECG-based authentication scheme that uses domain-based features. They found that the ECG data contained personal information about the subject and discussed the privacy leakage problem in ECG-based authentication. However, they did not present a detailed privacy leakage analysis of the existing authentication schemes.


### 2.2.2. Privacy-Preserving Authentication Schemes

Kang et al. [19] used a CNN-based generative adversarial networks (GAN) model in an ECG-based authentication scheme to generate an artificial ECG signal similar to an existing ECG signal. ECG signals contain sensitive individual information such as heart diseases. By utilizing the GAN model of personal information including the victim’s health status, their authentication scheme successfully eliminates heart disease and achieves a high authentication accuracy of 99.86%.Huang et al. [20] proposed a lightweight ECG-based authentication scheme that effectively protects personal information. They acquired ECG signal datasets from an online community and recruited 30 subjects. They obtained ECG signals from the subjects’ physical activities such as running and applied various ECG datasets to their model. They recorded electrocardiograms from online data sets and subjects performing various physical activities. Their proposed authentication scheme achieves the technique of differential privacy depending on the sensitivity of the data queries, making it impossible for an attacker to infer the patient’s ECG information. The scheme achieves high accuracy by applying soft thresholds while protecting privacy.Yang and Wang [21] proposed an ECG-based authentication scheme that can protect users’ personal information by using a noninvertible transformation called the manipulatable haar transform (MHT). They demonstrated the robustness of their authentication scheme against a variety of attacks, including replay attacks and brute force attacks. Their scheme also emphasizes efficiency by demonstrating the runtime and template size of the authentication scheme on IoT devices. Their proposed scheme can provide secure intranode authentication for wireless body sensor network (WBSN) and protect the user’s sensitive health information and user identity included in the ECG signal.Bhutra et al. [22] proposed a privacy-protecting biometric authentication scheme applicable in healthcare systems. Their authentication scheme performs ECG-based authentication in a complete end-to-end authentication system in the IoT ecosystem. They applied privacy-preserving algorithms based on previous research by Yao [23]. Their authentication protocol can protect users’ private information by modifying the distributed set intersection protocol [24].In the previous work, most of the research was conducted under the assumption that all of the system entities such as sensors and gateways of a health care system are safe. However, Nasr-Esfahani et al. [25] proposed a secure authentication method against internal and external attacks under the assumption that all entities of the healthcare system are untrusted. Their scheme can protect the user’s identity and the patient’s location by overcoming the mechanisms of Gope and Hwang [26] and the limitations of Wachsmann et al. [27], which are past studies. Their authentication technique can effectively facilitate mutual authentication between major institutions while protecting patient privacy and location. Their proposed scheme shows that it is safe from various attacks (inside attacks, external attacks) while protecting the patient’s privacy and location.


## 2.2.1. Vulnerability of ECG-Based Authentication Schemes

Simon et al. [14] presented successful attacks on biometric authentication schemes. More specifically, they spoofed ECG signals to the Nymi band, which is an ECG authentication application equipment used in practice. They also conducted mapping attacks by masquerading an authorized user by modifying the Nymi band system.Karimian et al. [15] presented an online and offline morphing attack using a single ECG beat as a template. Their attacks showed 90% accuracy for an online scenario with limited resources. While they addressed the risks of the existing ECG authentication schemes, this type of attack is not practical because multiple ECG channels are generally used to perform authentication.Lehmann and Buschek [16] proposed an ECG-based authentication scheme that can be used with a continuous electrocardiogram device. In their paper, they mentioned that certain private information, such as one’s health status and lifestyle, can be inferred from the ECG features. However, they did not present details on the privacy leakage problem of ECG-based authentication schemes.Bera et al. [17] proposed a robust security design for ECG-based authentication by combining ECG features with other biometric information. They claim that their design can successfully mitigate system and network attacks such as man-in-the-middle, replay, and privilege escalation attacks. However, they did not address the privacy leakage problems in ECG-based authentication schemes.Labati et al. [18] proposed an ECG-based authentication scheme that uses domain-based features. They found that the ECG data contained personal information about the subject and discussed the privacy leakage problem in ECG-based authentication. However, they did not present a detailed privacy leakage analysis of the existing authentication schemes.


## 2.2.2. Privacy-Preserving Authentication Schemes

Kang et al. [19] used a CNN-based generative adversarial networks (GAN) model in an ECG-based authentication scheme to generate an artificial ECG signal similar to an existing ECG signal. ECG signals contain sensitive individual information such as heart diseases. By utilizing the GAN model of personal information including the victim’s health status, their authentication scheme successfully eliminates heart disease and achieves a high authentication accuracy of 99.86%.Huang et al. [20] proposed a lightweight ECG-based authentication scheme that effectively protects personal information. They acquired ECG signal datasets from an online community and recruited 30 subjects. They obtained ECG signals from the subjects’ physical activities such as running and applied various ECG datasets to their model. They recorded electrocardiograms from online data sets and subjects performing various physical activities. Their proposed authentication scheme achieves the technique of differential privacy depending on the sensitivity of the data queries, making it impossible for an attacker to infer the patient’s ECG information. The scheme achieves high accuracy by applying soft thresholds while protecting privacy.Yang and Wang [21] proposed an ECG-based authentication scheme that can protect users’ personal information by using a noninvertible transformation called the manipulatable haar transform (MHT). They demonstrated the robustness of their authentication scheme against a variety of attacks, including replay attacks and brute force attacks. Their scheme also emphasizes efficiency by demonstrating the runtime and template size of the authentication scheme on IoT devices. Their proposed scheme can provide secure intranode authentication for wireless body sensor network (WBSN) and protect the user’s sensitive health information and user identity included in the ECG signal.Bhutra et al. [22] proposed a privacy-protecting biometric authentication scheme applicable in healthcare systems. Their authentication scheme performs ECG-based authentication in a complete end-to-end authentication system in the IoT ecosystem. They applied privacy-preserving algorithms based on previous research by Yao [23]. Their authentication protocol can protect users’ private information by modifying the distributed set intersection protocol [24].In the previous work, most of the research was conducted under the assumption that all of the system entities such as sensors and gateways of a health care system are safe. However, Nasr-Esfahani et al. [25] proposed a secure authentication method against internal and external attacks under the assumption that all entities of the healthcare system are untrusted. Their scheme can protect the user’s identity and the patient’s location by overcoming the mechanisms of Gope and Hwang [26] and the limitations of Wachsmann et al. [27], which are past studies. Their authentication technique can effectively facilitate mutual authentication between major institutions while protecting patient privacy and location. Their proposed scheme shows that it is safe from various attacks (inside attacks, external attacks) while protecting the patient’s privacy and location.


## 3. Privacy Leakage Analysis

In this section, we analyze the privacy leakage problems in existing fiducial points-based ECG authentication schemes.


### 3.1. Motivation

As fiducial points in the ECG data contain sufficient information for personal identification, fiducial points-based ECG authentication methods achieve higher accuracy than signal-based methods. However, the higher accuracy of the identification comes with the risk of privacy leakage, as personal information can be obtained from fiducial points used in the authentication. Thus, we are motivated to answer the following research questions:(i)
(Q1) How much personal information can an attacker obtain with the given features in an authentication template?(ii)
(Q2) How can we achieve secure fiducial points-based authentication that prevents such privacy leakage?We answer the question Q1 in this section, while Q2 is addressed in the next section (cf. Section4). Regarding question Q1, we extensively investigated a variety of existing authentication schemes that utilize fiducial points as features for identification. All authentication schemes that we investigated commonly use ECG lead II to obtain features, while the number of features (i.e., fiducial points) varies between 8 and 36.To analyze privacy leakage from these authentication schemes, we constructed classification models using machine learning and deep learning algorithms. We describe the construction of the classification models and the analysis results in the following subsections.


### 3.2. Constructing Classification Model

To answer question Q1, we constructed four classification models using naive Bayes, decision trees, random forest, and convolutional neural network (CNN) algorithms. Using these models, we attempted to deduce and classify several pieces of private information, such as age, gender, and disease status, from features used in the authentication schemes. Our experiments for the privacy leakage analysis were conducted under Ubuntu 20.04 LTS (64-bit), equipped with an Intel i9-10900K and an RTX 3090 GPGPU (Cuda v10.1.2.43), where TensorFlow 2.4.1 and Python 3.8.5 were used.Figure2 illustrates the overall process of our analysis, which consisted of a training phase and an attack phase, of private leakage using the classification model.Training phase. We assume that an attacker might obtain raw ECG signal data from public databases such as Physionet. The public dataset contains specific data of subjects who suffer from heart diseases such as arrhythmias, as well as ECG data of subjects who have no disease. Therefore, the attacker can train and implement classification models to leak private information using public ECG data.Attack phase. An attacker generates attack models (i.e., classification models) using public databases, as described above. We assume that the attacker obtains information used for ECG-based authentication, such as authentication features (i.e., ECG fiducial points) and any auxiliary data; he/she might obtain the information by compromising an authentication database or capturing data during authentication. With the obtained fiducial points, the attacker attempts to infer private information using classification models.Figure 2
Block diagram of ECG privacy leakage classification.


#### 3.2.1. ECG Dataset

We need an ECG dataset to construct our classification model. Fortunately, there are some public datasets, such as Physionet [28], available for research purposes. This public dataset includes not only ECG data of patients who suffer from heart disease (e.g., arrhythmia) but also ECG data of subjects without the disease. Each record in the dataset contains the raw data on the heart rhythm of a subject as well as auxiliary information, such as the age and gender of the subject, and even the name of the disease that he/she has. We used the public dataset to train and verify our classification models.As the ECG patterns of patients with diseases are significantly different from those of healthy people, we need to construct two kinds of classification models, one for the patients and the other for all subjects. Hence, we created two datasets by dividing the public dataset according to the disease information of the subjects; one is a dataset that consists of patients’ ECG data, while the other dataset contains all ECG data for both patients and healthy people. We trained each classification model with the corresponding dataset.


#### 3.2.2. Feature Extraction

We need to extract fiducial points from the raw ECG data to construct the classification models. In general, fiducial points extracted from ECGs are used for authentication because they clearly differ between individuals. As the number of heartbeats is different for each subject, the number of segments (i.e., the number of P-QRS-T cycles) in the raw ECG data also varies, even if the ECGs were measured for the same time period. In other words, as the collected raw ECG data depends on the measurement environment and the subject, the heart rate is measured and stored differently in the data set.All features used in the existing fiducial-point-based authentication schemes are listed in Table2. Our method for extracting features from raw ECG data proceeds in four steps, which are described as follows.Step 1Preprocessing raw ECG Data. As a first step, we need to preprocess the raw ECG data as it may contain some noise. Noise can be eliminated using a filter such as FIR.Step 2Extracting R-peak point. After the noise has been removed, the R-peak is extracted for all segments from the ECG data. As the R-peak is the most distinctive point in a heart’s electrical activation pattern, it can easily be recognized by simply comparing the relative sizes of the QRS regions. The R-peak can also be obtained by determining the maximum and minimum values of the array of ECG data points for the QRS values. [29].Step 3Extracting P, Q, S, and T-peak points. After the R-peaks have been extracted, the P, Q, S, and T peaks are extracted using a wavelet transform based on the R-peak point [30, 31].Step 4Extracting the remaining fiducial points. As a final step, the remaining 45 fiducial points are extracted from the previous results. Specifically, the remaining points can be calculated from the extracted fiducial points (i.e., R-peak, P, Q, S, and T-peak) using the expressions presented in Table 2.Table 2
Extracted and sorted ECG features in lead II.
NoFiducial pointExpressionRepresentation1P-wave amp—Pamp2Q-wave amp—Qamp3R-wave amp—Ramp4S-wave amp—Samp5T-wave amp—Tamp6Q-waves amp perQamp/Ramp∗100Qampper7S-waves amp perSamp/Ramp∗100Sampper8T-waves amp perTamp/Ramp∗100Tampper9PR-amp diffPamp−RampPRamp10RT-amp differenceRamp−TampRTamp11QT-amp diffQamp−TampQTamp12PT-amp diffPamp−TampPTamp13QR-amp diffQamp−RampQRamp14RS-amp diffRamp−SampRSamp15RT-amp differenceRamp−TampRTamp16QT-amp diffQamp−TampQTamp17PT-amp diffPamp−TampPTamp18ST-amp diffSamp−TampSTamp19PQ amp diffPamp−QampPQamp20PQ intervalPpeaktoQpeakPQinterval21PR intervalPpeaktoRpeakPRinterval22PS intervalPpeaktoSpeakPSinterval23QR intervalQpeaktoRpeakQRinterval24QT intervalQpeaktoTpeakQTinterval25RR intervalRpeaktoRpeakRRinterval26RS intervalRpeaktoSpeakRSinterval27RT intervalRpeaktoTpeakRTinterval28ST intervalSpeaktoTpeakSTinterval29P-onset—Ponset30T-offset—Toffset31QTc (Bazzet)QTinterval/RRintervalQTc32PS peaks multiPpeak∗SpeakPSmulti33PT peaks multiPpeak∗TpeakPTmulti34QT peaks multiQpeak∗TpeakQTmulti35RT-peak multiRpeak∗TpeakRTmulti36ST-peak multiSpeak∗TpeakSTmulti37New-PQ intervalPQinterval/RpeakNPQinterval38New-PR intervalPRinterval/RpeakNPRinterval39New-PS intervalPSinterval/RpeakNPSinterval40New-QR intervalQRinterval/RpeakNQRinterval41New-QT intervalQTinterval/RpeakNQTinterval42New-RR intervalRRinterval/RpeakNRRinterval43New-RS intervalRSinterval/RpeakNRSinterval44New-RT intervalRTinterval/RpeakNRTinterval45New-ST intervalSTinterval/RpeakNSTinterval


#### 3.2.3. ECG Data Analysis

To evaluate our classification models, we divided the dataset into a training set and a test set, whereby the training and test sets were comprised of 80% and 20% of the entire dataset, respectively. We applied the following rules when dividing the dataset:(i)
Considering that the ECG data changes with time, information extracted from one source of the signal data should be exclusively included in either the training or test set.(ii)
The number of samples for each subject should be balanced between those two data sets; if necessary, more samples are generated by sub-sampling.For the experiment, we used the MIT-BIH database from Physionet. The MIT-BIH database consists of the raw ECG data of 48 participants, consisting of 26 males and 22 females. The age of the participants varied between 20 and 80, although most of them were over 60 years old. The database includes a total of 1,000 ECG signals from 48 subjects, which were obtained through the ECG Lead II method; the signal was extracted for 30 min at 360 Hz.After analyzing the ECG signals, we found that fiducial points from five subjects could not be successfully extracted. Therefore, we used 990 out of 1,000 ECG signals, excluding those five subjects, and extracted peak points from the signal data.


### 3.3. Analysis

We analyzed the existing ECG-based authentication schemes regarding their privacy leakage using our classification models: naive Bayes, decision tree, random forest, and CNN. More specifically, we attempted to infer private information, such as gender, age, and disease status from the features that were used to authenticate each subject. For the analysis, we used the authentication features used in the schemes as inputs for our classification models. We applied 5-fold cross-validation and obtained the averaged results by repeating 20 runs of all experiments for each authentication scheme.Table3 presents a summary of our analysis results. Only the classification results that show the highest accuracy for each type of information are presented in this table. Furthermore, we present the analysis results of each classification model in detail.Naive Bayes model. The naive Bayes model shows the lowest accuracy among the four classification models. In particular, this model classifies gender with a 70% accuracy, age with a 47%, and disease with a 53% accuracy. We reason that the relatively low accuracy is due to the features, that is, fiducial points, that the authentication schemes use, which are mutually dependent on each other. As the Gaussian model only guarantees a high accuracy when features are independent variables, fiducial points-based authentication schemes show a relatively low classification accuracy.Decision Tree model. This model shows a 92% accuracy for classifying gender, an 85% for age, and a 76% accuracy for disease. We adjusted the hyperparameters in this model by applying 5-fold cross-validation and verified the classification results for each class using a confusion matrix. The verification results guarantee that the true positives and false negatives of the model have an accuracy of nearly 90%. As a result of checking all investigations on personal privacy leakage, this model shows the largest difference in accuracy between the training and test sets.Random Forest model. As the decision tree model has an overfitting problem, we implemented a random forest model for our analysis. The random forest model shows optimal performance in classifying the gender and age categories. Using the same setting as in the decision tree model, it shows an accuracy of 98% for gender, 93% for age category, and 84% for disease. This model has relatively low accuracy in classifying disease information. We reason that this is because the disease information includes several classes, such as ten arrhythmia diseases and information on healthy people, which implies that a more precise model is necessary.CNN model. We used CNN, a deep learning algorithm to create a model that effectively classifies a large amount of information. Details on the model are presented in Table 4. Our CNN model has an accuracy of 93% for gender, 90% for age group, and 87% for disease. As the CNN model can classify various kinds of diseases, it shows the optimal performance for classifying disease rather than the gender and age categories.Summary. To sum up, our experiments using various classification models show that private information can be leaked from authentication features in fiducial point-based authentication schemes. Through analysis, we find that our random forest model provides the best performance for classifying the gender and age categories. We would like to emphasize that our CNN model also shows optimal results for classifying the age and disease categories.Table 3
Results of privacy leakage analysis of fiducial points-based ECG authentication schemes.
NoAuthentication schemesFiducial points used in the schemeRemarkGenderAgeDisease (total)Disease (unhealthy)1Kim et al. [32](i) Amplitude of PR, QR, RS, RT99.62% (R.F)99.44% (CNN)89.1% (CNN)86.97% (CNN)(ii) Interval of PR, QR, RS, RT, ST, RR2Wang et al. [33](i) Amplitude of P, T99.07% (CNN)99.95% (R.F)81.18% (CNN)85.3% (CNN)(ii) Interval of PR, PP, QT3Bassiouni et al. [34](i) Amplitude of QR, PT, PS, PQ98.92% (R.F)98.55% (R.F)88.93% (CNN)89.7% (CNN)(ii) Amplitude of PR, RT, QS, QT, ST(iii) Interval of QR, RS, PQ, ST, PR, RT, PQ, ST4Choi et al. [35](i) Amplitude of P, Q, R, S, T96.82% (R.F)97.2% (R.F)89.17% (CNN)89.51% (CNN)(ii) Interval of PQ, ST5Singh et al. [36](i) Interval of PP, QQ, SS, TT, RR98.88% (R.F)96.83% (R.F)79.43% (R.F)82.2% (R.F)6Keshishzadeh and Rashidi [37](i) Amplitude of R, T, QR, PR, RT, QT, PT, QR, SR96.3% (R.F)98.92% (R.F)90.12% (CNN)91.45% (CNN)(ii) Interval of PS, RS, PR, RT, ST, PQ(iii) Multiplication of RT, PT, QT, ST, PS(iv) amplitude percent of T, S, Q7Singh and Singh [38](i) Interval of P, T, Q, S, PR, ST, QT, RR99.25% (R.F)98.69% (R.F)85.67% (CNN)85.67% (CNN)8David Shen et al. [39](i) Amplitude of QR, RS, ST, PQ94.68% (R.F)95.15% (R.F)92.07% (CNN)91.16% (CNN)9Venkatesh and Jayaraman [40](i) Interval of P, T, Q, S, PR, ST, QT, RR99.07% (R.F)98.5% (R.F)84.42% (CNN)84.4% (CNN)10Fang and Chan [41](i) Amplitude of PR, QR, SR, TR99.44% (R.F)99.07% (R.F)88.56% (CNN)83.82% (CNN)(ii) Interval of PR, QR, ST, TRTable 4
Hyperparameters for the deep learning classifier.
Hyper parameterGenderAgeDiseaseBatch size202016Cross validation555Convolution activationReluReluReluConvolution layer334Input# of features used in each schemeLearning rate0.0010.0010.001Loss functionSparse categorical cross entropyOptimizerAdamAdamAdamTraining epochEarly stop by validation accuracyFurthermore, we find that there are some fiducial points that are more sensitive to privacy leakage. This implies that even if a small number of fiducial points are used in the authentication, an attacker can successfully leak private information from these features with high accuracy.


## 3.1. Motivation

As fiducial points in the ECG data contain sufficient information for personal identification, fiducial points-based ECG authentication methods achieve higher accuracy than signal-based methods. However, the higher accuracy of the identification comes with the risk of privacy leakage, as personal information can be obtained from fiducial points used in the authentication. Thus, we are motivated to answer the following research questions:(i)
(Q1) How much personal information can an attacker obtain with the given features in an authentication template?(ii)
(Q2) How can we achieve secure fiducial points-based authentication that prevents such privacy leakage?We answer the question Q1 in this section, while Q2 is addressed in the next section (cf. Section4). Regarding question Q1, we extensively investigated a variety of existing authentication schemes that utilize fiducial points as features for identification. All authentication schemes that we investigated commonly use ECG lead II to obtain features, while the number of features (i.e., fiducial points) varies between 8 and 36.To analyze privacy leakage from these authentication schemes, we constructed classification models using machine learning and deep learning algorithms. We describe the construction of the classification models and the analysis results in the following subsections.


## 3.2. Constructing Classification Model

To answer question Q1, we constructed four classification models using naive Bayes, decision trees, random forest, and convolutional neural network (CNN) algorithms. Using these models, we attempted to deduce and classify several pieces of private information, such as age, gender, and disease status, from features used in the authentication schemes. Our experiments for the privacy leakage analysis were conducted under Ubuntu 20.04 LTS (64-bit), equipped with an Intel i9-10900K and an RTX 3090 GPGPU (Cuda v10.1.2.43), where TensorFlow 2.4.1 and Python 3.8.5 were used.Figure2 illustrates the overall process of our analysis, which consisted of a training phase and an attack phase, of private leakage using the classification model.Training phase. We assume that an attacker might obtain raw ECG signal data from public databases such as Physionet. The public dataset contains specific data of subjects who suffer from heart diseases such as arrhythmias, as well as ECG data of subjects who have no disease. Therefore, the attacker can train and implement classification models to leak private information using public ECG data.Attack phase. An attacker generates attack models (i.e., classification models) using public databases, as described above. We assume that the attacker obtains information used for ECG-based authentication, such as authentication features (i.e., ECG fiducial points) and any auxiliary data; he/she might obtain the information by compromising an authentication database or capturing data during authentication. With the obtained fiducial points, the attacker attempts to infer private information using classification models.Figure 2
Block diagram of ECG privacy leakage classification.


### 3.2.1. ECG Dataset

We need an ECG dataset to construct our classification model. Fortunately, there are some public datasets, such as Physionet [28], available for research purposes. This public dataset includes not only ECG data of patients who suffer from heart disease (e.g., arrhythmia) but also ECG data of subjects without the disease. Each record in the dataset contains the raw data on the heart rhythm of a subject as well as auxiliary information, such as the age and gender of the subject, and even the name of the disease that he/she has. We used the public dataset to train and verify our classification models.As the ECG patterns of patients with diseases are significantly different from those of healthy people, we need to construct two kinds of classification models, one for the patients and the other for all subjects. Hence, we created two datasets by dividing the public dataset according to the disease information of the subjects; one is a dataset that consists of patients’ ECG data, while the other dataset contains all ECG data for both patients and healthy people. We trained each classification model with the corresponding dataset.


### 3.2.2. Feature Extraction

We need to extract fiducial points from the raw ECG data to construct the classification models. In general, fiducial points extracted from ECGs are used for authentication because they clearly differ between individuals. As the number of heartbeats is different for each subject, the number of segments (i.e., the number of P-QRS-T cycles) in the raw ECG data also varies, even if the ECGs were measured for the same time period. In other words, as the collected raw ECG data depends on the measurement environment and the subject, the heart rate is measured and stored differently in the data set.All features used in the existing fiducial-point-based authentication schemes are listed in Table2. Our method for extracting features from raw ECG data proceeds in four steps, which are described as follows.Step 1Preprocessing raw ECG Data. As a first step, we need to preprocess the raw ECG data as it may contain some noise. Noise can be eliminated using a filter such as FIR.Step 2Extracting R-peak point. After the noise has been removed, the R-peak is extracted for all segments from the ECG data. As the R-peak is the most distinctive point in a heart’s electrical activation pattern, it can easily be recognized by simply comparing the relative sizes of the QRS regions. The R-peak can also be obtained by determining the maximum and minimum values of the array of ECG data points for the QRS values. [29].Step 3Extracting P, Q, S, and T-peak points. After the R-peaks have been extracted, the P, Q, S, and T peaks are extracted using a wavelet transform based on the R-peak point [30, 31].Step 4Extracting the remaining fiducial points. As a final step, the remaining 45 fiducial points are extracted from the previous results. Specifically, the remaining points can be calculated from the extracted fiducial points (i.e., R-peak, P, Q, S, and T-peak) using the expressions presented in Table 2.Table 2
Extracted and sorted ECG features in lead II.
NoFiducial pointExpressionRepresentation1P-wave amp—Pamp2Q-wave amp—Qamp3R-wave amp—Ramp4S-wave amp—Samp5T-wave amp—Tamp6Q-waves amp perQamp/Ramp∗100Qampper7S-waves amp perSamp/Ramp∗100Sampper8T-waves amp perTamp/Ramp∗100Tampper9PR-amp diffPamp−RampPRamp10RT-amp differenceRamp−TampRTamp11QT-amp diffQamp−TampQTamp12PT-amp diffPamp−TampPTamp13QR-amp diffQamp−RampQRamp14RS-amp diffRamp−SampRSamp15RT-amp differenceRamp−TampRTamp16QT-amp diffQamp−TampQTamp17PT-amp diffPamp−TampPTamp18ST-amp diffSamp−TampSTamp19PQ amp diffPamp−QampPQamp20PQ intervalPpeaktoQpeakPQinterval21PR intervalPpeaktoRpeakPRinterval22PS intervalPpeaktoSpeakPSinterval23QR intervalQpeaktoRpeakQRinterval24QT intervalQpeaktoTpeakQTinterval25RR intervalRpeaktoRpeakRRinterval26RS intervalRpeaktoSpeakRSinterval27RT intervalRpeaktoTpeakRTinterval28ST intervalSpeaktoTpeakSTinterval29P-onset—Ponset30T-offset—Toffset31QTc (Bazzet)QTinterval/RRintervalQTc32PS peaks multiPpeak∗SpeakPSmulti33PT peaks multiPpeak∗TpeakPTmulti34QT peaks multiQpeak∗TpeakQTmulti35RT-peak multiRpeak∗TpeakRTmulti36ST-peak multiSpeak∗TpeakSTmulti37New-PQ intervalPQinterval/RpeakNPQinterval38New-PR intervalPRinterval/RpeakNPRinterval39New-PS intervalPSinterval/RpeakNPSinterval40New-QR intervalQRinterval/RpeakNQRinterval41New-QT intervalQTinterval/RpeakNQTinterval42New-RR intervalRRinterval/RpeakNRRinterval43New-RS intervalRSinterval/RpeakNRSinterval44New-RT intervalRTinterval/RpeakNRTinterval45New-ST intervalSTinterval/RpeakNSTinterval


### 3.2.3. ECG Data Analysis

To evaluate our classification models, we divided the dataset into a training set and a test set, whereby the training and test sets were comprised of 80% and 20% of the entire dataset, respectively. We applied the following rules when dividing the dataset:(i)
Considering that the ECG data changes with time, information extracted from one source of the signal data should be exclusively included in either the training or test set.(ii)
The number of samples for each subject should be balanced between those two data sets; if necessary, more samples are generated by sub-sampling.For the experiment, we used the MIT-BIH database from Physionet. The MIT-BIH database consists of the raw ECG data of 48 participants, consisting of 26 males and 22 females. The age of the participants varied between 20 and 80, although most of them were over 60 years old. The database includes a total of 1,000 ECG signals from 48 subjects, which were obtained through the ECG Lead II method; the signal was extracted for 30 min at 360 Hz.After analyzing the ECG signals, we found that fiducial points from five subjects could not be successfully extracted. Therefore, we used 990 out of 1,000 ECG signals, excluding those five subjects, and extracted peak points from the signal data.


## 3.2.1. ECG Dataset

We need an ECG dataset to construct our classification model. Fortunately, there are some public datasets, such as Physionet [28], available for research purposes. This public dataset includes not only ECG data of patients who suffer from heart disease (e.g., arrhythmia) but also ECG data of subjects without the disease. Each record in the dataset contains the raw data on the heart rhythm of a subject as well as auxiliary information, such as the age and gender of the subject, and even the name of the disease that he/she has. We used the public dataset to train and verify our classification models.As the ECG patterns of patients with diseases are significantly different from those of healthy people, we need to construct two kinds of classification models, one for the patients and the other for all subjects. Hence, we created two datasets by dividing the public dataset according to the disease information of the subjects; one is a dataset that consists of patients’ ECG data, while the other dataset contains all ECG data for both patients and healthy people. We trained each classification model with the corresponding dataset.


## 3.2.2. Feature Extraction

We need to extract fiducial points from the raw ECG data to construct the classification models. In general, fiducial points extracted from ECGs are used for authentication because they clearly differ between individuals. As the number of heartbeats is different for each subject, the number of segments (i.e., the number of P-QRS-T cycles) in the raw ECG data also varies, even if the ECGs were measured for the same time period. In other words, as the collected raw ECG data depends on the measurement environment and the subject, the heart rate is measured and stored differently in the data set.All features used in the existing fiducial-point-based authentication schemes are listed in Table2. Our method for extracting features from raw ECG data proceeds in four steps, which are described as follows.Step 1Preprocessing raw ECG Data. As a first step, we need to preprocess the raw ECG data as it may contain some noise. Noise can be eliminated using a filter such as FIR.Step 2Extracting R-peak point. After the noise has been removed, the R-peak is extracted for all segments from the ECG data. As the R-peak is the most distinctive point in a heart’s electrical activation pattern, it can easily be recognized by simply comparing the relative sizes of the QRS regions. The R-peak can also be obtained by determining the maximum and minimum values of the array of ECG data points for the QRS values. [29].Step 3Extracting P, Q, S, and T-peak points. After the R-peaks have been extracted, the P, Q, S, and T peaks are extracted using a wavelet transform based on the R-peak point [30, 31].Step 4Extracting the remaining fiducial points. As a final step, the remaining 45 fiducial points are extracted from the previous results. Specifically, the remaining points can be calculated from the extracted fiducial points (i.e., R-peak, P, Q, S, and T-peak) using the expressions presented in Table 2.Table 2
Extracted and sorted ECG features in lead II.
NoFiducial pointExpressionRepresentation1P-wave amp—Pamp2Q-wave amp—Qamp3R-wave amp—Ramp4S-wave amp—Samp5T-wave amp—Tamp6Q-waves amp perQamp/Ramp∗100Qampper7S-waves amp perSamp/Ramp∗100Sampper8T-waves amp perTamp/Ramp∗100Tampper9PR-amp diffPamp−RampPRamp10RT-amp differenceRamp−TampRTamp11QT-amp diffQamp−TampQTamp12PT-amp diffPamp−TampPTamp13QR-amp diffQamp−RampQRamp14RS-amp diffRamp−SampRSamp15RT-amp differenceRamp−TampRTamp16QT-amp diffQamp−TampQTamp17PT-amp diffPamp−TampPTamp18ST-amp diffSamp−TampSTamp19PQ amp diffPamp−QampPQamp20PQ intervalPpeaktoQpeakPQinterval21PR intervalPpeaktoRpeakPRinterval22PS intervalPpeaktoSpeakPSinterval23QR intervalQpeaktoRpeakQRinterval24QT intervalQpeaktoTpeakQTinterval25RR intervalRpeaktoRpeakRRinterval26RS intervalRpeaktoSpeakRSinterval27RT intervalRpeaktoTpeakRTinterval28ST intervalSpeaktoTpeakSTinterval29P-onset—Ponset30T-offset—Toffset31QTc (Bazzet)QTinterval/RRintervalQTc32PS peaks multiPpeak∗SpeakPSmulti33PT peaks multiPpeak∗TpeakPTmulti34QT peaks multiQpeak∗TpeakQTmulti35RT-peak multiRpeak∗TpeakRTmulti36ST-peak multiSpeak∗TpeakSTmulti37New-PQ intervalPQinterval/RpeakNPQinterval38New-PR intervalPRinterval/RpeakNPRinterval39New-PS intervalPSinterval/RpeakNPSinterval40New-QR intervalQRinterval/RpeakNQRinterval41New-QT intervalQTinterval/RpeakNQTinterval42New-RR intervalRRinterval/RpeakNRRinterval43New-RS intervalRSinterval/RpeakNRSinterval44New-RT intervalRTinterval/RpeakNRTinterval45New-ST intervalSTinterval/RpeakNSTinterval


## 3.2.3. ECG Data Analysis

To evaluate our classification models, we divided the dataset into a training set and a test set, whereby the training and test sets were comprised of 80% and 20% of the entire dataset, respectively. We applied the following rules when dividing the dataset:(i)
Considering that the ECG data changes with time, information extracted from one source of the signal data should be exclusively included in either the training or test set.(ii)
The number of samples for each subject should be balanced between those two data sets; if necessary, more samples are generated by sub-sampling.For the experiment, we used the MIT-BIH database from Physionet. The MIT-BIH database consists of the raw ECG data of 48 participants, consisting of 26 males and 22 females. The age of the participants varied between 20 and 80, although most of them were over 60 years old. The database includes a total of 1,000 ECG signals from 48 subjects, which were obtained through the ECG Lead II method; the signal was extracted for 30 min at 360 Hz.After analyzing the ECG signals, we found that fiducial points from five subjects could not be successfully extracted. Therefore, we used 990 out of 1,000 ECG signals, excluding those five subjects, and extracted peak points from the signal data.


## 3.3. Analysis

We analyzed the existing ECG-based authentication schemes regarding their privacy leakage using our classification models: naive Bayes, decision tree, random forest, and CNN. More specifically, we attempted to infer private information, such as gender, age, and disease status from the features that were used to authenticate each subject. For the analysis, we used the authentication features used in the schemes as inputs for our classification models. We applied 5-fold cross-validation and obtained the averaged results by repeating 20 runs of all experiments for each authentication scheme.Table3 presents a summary of our analysis results. Only the classification results that show the highest accuracy for each type of information are presented in this table. Furthermore, we present the analysis results of each classification model in detail.Naive Bayes model. The naive Bayes model shows the lowest accuracy among the four classification models. In particular, this model classifies gender with a 70% accuracy, age with a 47%, and disease with a 53% accuracy. We reason that the relatively low accuracy is due to the features, that is, fiducial points, that the authentication schemes use, which are mutually dependent on each other. As the Gaussian model only guarantees a high accuracy when features are independent variables, fiducial points-based authentication schemes show a relatively low classification accuracy.Decision Tree model. This model shows a 92% accuracy for classifying gender, an 85% for age, and a 76% accuracy for disease. We adjusted the hyperparameters in this model by applying 5-fold cross-validation and verified the classification results for each class using a confusion matrix. The verification results guarantee that the true positives and false negatives of the model have an accuracy of nearly 90%. As a result of checking all investigations on personal privacy leakage, this model shows the largest difference in accuracy between the training and test sets.Random Forest model. As the decision tree model has an overfitting problem, we implemented a random forest model for our analysis. The random forest model shows optimal performance in classifying the gender and age categories. Using the same setting as in the decision tree model, it shows an accuracy of 98% for gender, 93% for age category, and 84% for disease. This model has relatively low accuracy in classifying disease information. We reason that this is because the disease information includes several classes, such as ten arrhythmia diseases and information on healthy people, which implies that a more precise model is necessary.CNN model. We used CNN, a deep learning algorithm to create a model that effectively classifies a large amount of information. Details on the model are presented in Table 4. Our CNN model has an accuracy of 93% for gender, 90% for age group, and 87% for disease. As the CNN model can classify various kinds of diseases, it shows the optimal performance for classifying disease rather than the gender and age categories.Summary. To sum up, our experiments using various classification models show that private information can be leaked from authentication features in fiducial point-based authentication schemes. Through analysis, we find that our random forest model provides the best performance for classifying the gender and age categories. We would like to emphasize that our CNN model also shows optimal results for classifying the age and disease categories.Table 3
Results of privacy leakage analysis of fiducial points-based ECG authentication schemes.
NoAuthentication schemesFiducial points used in the schemeRemarkGenderAgeDisease (total)Disease (unhealthy)1Kim et al. [32](i) Amplitude of PR, QR, RS, RT99.62% (R.F)99.44% (CNN)89.1% (CNN)86.97% (CNN)(ii) Interval of PR, QR, RS, RT, ST, RR2Wang et al. [33](i) Amplitude of P, T99.07% (CNN)99.95% (R.F)81.18% (CNN)85.3% (CNN)(ii) Interval of PR, PP, QT3Bassiouni et al. [34](i) Amplitude of QR, PT, PS, PQ98.92% (R.F)98.55% (R.F)88.93% (CNN)89.7% (CNN)(ii) Amplitude of PR, RT, QS, QT, ST(iii) Interval of QR, RS, PQ, ST, PR, RT, PQ, ST4Choi et al. [35](i) Amplitude of P, Q, R, S, T96.82% (R.F)97.2% (R.F)89.17% (CNN)89.51% (CNN)(ii) Interval of PQ, ST5Singh et al. [36](i) Interval of PP, QQ, SS, TT, RR98.88% (R.F)96.83% (R.F)79.43% (R.F)82.2% (R.F)6Keshishzadeh and Rashidi [37](i) Amplitude of R, T, QR, PR, RT, QT, PT, QR, SR96.3% (R.F)98.92% (R.F)90.12% (CNN)91.45% (CNN)(ii) Interval of PS, RS, PR, RT, ST, PQ(iii) Multiplication of RT, PT, QT, ST, PS(iv) amplitude percent of T, S, Q7Singh and Singh [38](i) Interval of P, T, Q, S, PR, ST, QT, RR99.25% (R.F)98.69% (R.F)85.67% (CNN)85.67% (CNN)8David Shen et al. [39](i) Amplitude of QR, RS, ST, PQ94.68% (R.F)95.15% (R.F)92.07% (CNN)91.16% (CNN)9Venkatesh and Jayaraman [40](i) Interval of P, T, Q, S, PR, ST, QT, RR99.07% (R.F)98.5% (R.F)84.42% (CNN)84.4% (CNN)10Fang and Chan [41](i) Amplitude of PR, QR, SR, TR99.44% (R.F)99.07% (R.F)88.56% (CNN)83.82% (CNN)(ii) Interval of PR, QR, ST, TRTable 4
Hyperparameters for the deep learning classifier.
Hyper parameterGenderAgeDiseaseBatch size202016Cross validation555Convolution activationReluReluReluConvolution layer334Input# of features used in each schemeLearning rate0.0010.0010.001Loss functionSparse categorical cross entropyOptimizerAdamAdamAdamTraining epochEarly stop by validation accuracyFurthermore, we find that there are some fiducial points that are more sensitive to privacy leakage. This implies that even if a small number of fiducial points are used in the authentication, an attacker can successfully leak private information from these features with high accuracy.


## 4. Privacy-Preserving Authentication

In this section, we answer the research question Q2 mentioned in Section3.1. Specifically, we propose a new ECG authentication scheme that is secure against the privacy leakage attacks that we presented in the previous section. The basic idea of our method is to construct a privacy-preserving authentication scheme with fiducial points that are the least sensitive to private information. We describe a method for determining such fiducial points and present our privacy-preserving authentication scheme as well as describe its evaluation.


### 4.1. The Proposed Scheme

The overall procedure to construct the proposed privacy-preserving authentication scheme is illustrated in Figure3. As a first step in building the proposed scheme, we look for fiducial points that are the least sensitive to privacy leakage by reorganizing our database. Electrocardiogram intervals are one of the fiducial points that are essential for personal identification, but they also contain sufficient private information to classify diseases [42, 43]. Instead of using such fiducial points, we find eight new fiducial points (i.e., the 37-th ∼45-th fiducial points listed in Table 2), which eliminate the possibility of information leakage. We could figure out such fiducial points successfully by dividing existing interval values by the “R” peak point and replacing this value with the original P-QRS complex-T interval for personal authentication.Figure 3
Constructing a privacy-preserving authentication scheme.Moreover, we also determine the optimal fiducial points for privacy-preserving authentication using correlation coefficients and a genetic algorithm. In order to evaluate the quality of our proposed system, we disclose the parameters used for our algorithm. We adjust hyperparameters used for the genetic algorithm: population size, maximum replication, mutation rate, and the probability of fitness individuals. Table5 shows the parameters we used.Table 5
The initial values for parameters on the genetic algorithm.
ParameterValuesPopulation400Crossover probability0.5Crossover independent0.8Mutation probability0.01Mutation probability0.01Tournament size3


#### 4.1.1. Correlation Coefficient Method

First, we use Pearson’s correlation to search forprivacy-sensitive fiducial points, which include privacy-related information, such as gender, age, and disease status. As a result of the Pearson correlation-based search, we found that fiducial points “R” and “Q” are sensitive to gender, while points “S” and “T” are less sensitive. In terms of the information about age, the fiducial points “PR,” “RS” intervals, and “S,” “Q” amplitudes are privacy-sensitive, while the “Q,” “S,” and “T” amplitudes are less sensitive.


#### 4.1.2. Genetic Algorithm-Based Method

We also apply a genetic algorithm [44, 45] to construct our privacy-preserving ECG-based authentication method. A genetic algorithm is usually used to solve optimization problems that cannot be clearly defined without complex formulas. In our study, we applied a genetic algorithm to find privacy-sensitive fiducial points. In particular, we constructed an algorithm that ranks fiducial points based on their sensitivity to private information, such as gender, age, and disease status. The algorithm proceeds in five steps, which are presented in detail below.Step 1Initial Generation. The more fiducial points are used, the more sensitive the disease-sensitive fiducial points can be analyzed, but on the contrary, it takes a lot of time to calculate. We use all 45 fiducial points described in Table 2 to analyze the impact of the fiducial points.Step 2Selection. The roulette wheel selection method is applied in this step. By applying this method, we adjust a method that maximizes the probability of selecting fiducial points with higher fitness according to privacy. However, we have always opened the possibility of extracting various fiducial points for each personal information.Step 3Crossover. The multipoint crossover method is applied in this step. There are many ways to find the optimal fiducial points for each privacy information. However, we set the parental trait to be properly inherited by the offspring by using the multipoint crossover method in the crossover process.Step 4Mutation. The uniform mutation method is applied in this step. In general, the uniform mutation reduces the accuracy of fitness fiducial points for each privacy information. However, the mutation process generates properties that are not present in the parent solution, maximizing the effectiveness of the global search ability.Step 5Replacement. In this step, the algorithm compares the newly generated offspring with the parents and substitutes the low-fitness fiducial points for each privacy information.The steps 2–5 described above are performed once in a single iteration, and the iteration is repeated 400 times for each privacy information. As a result of the algorithm, all the fiducial points are listed in order according to their privacy sensitivity. Then, we remove the top-most ranked fiducial points, as they are not appropriate for authentication features in our privacy-preserving authentication scheme. This process is repeated until all the fiducial points are cleared for each privacy information.Table6 presents the results of the feature selection using our genetic algorithm. The fiducial points which are privacy-insensitive are listed in “Remnant” in the table. The results show that the fiducial point “R” is highly sensitive to gender, while the “PS-complex”values are relatively less sensitive to private information. With regards to age, fiducial points “T” and “RS-complex” are sensitive, while “PS” and “PT” are less sensitive. Fiducial point “T” is highly sensitive to disease, while “R & Q” are less sensitive to this information. Our findings regarding the disease status are supported by other works, in which methods for diagnosing diseases such as myocardial infraction [46] and unstable angina [47] were proposed using the fiducial point “T.” Our results imply that any ECG-based authentication scheme that uses privacy-sensitive fiducial points is vulnerable to privacy leakage attacks.Table 6
Feature selection results using the genetic algorithm.
IterationGenderAgeDisease1PampRSampQampNQRintervalQTampTampNRSintervalSTampToffsetNRTintervalNRSintervalSTmulti2TampPQampPSintervalPonsetNPQintervalTampperRSampNPSintervalSampperPTmultiRTmultiNRRintervalQSamp3RTampPampPSmultiQampQampPTmultiTampperPonset4QTmultiPRampSTampNPRintervalQTmultiRTmultiQampper5ToffsetNQRintervalQRampSampQSampRTamp6RTmultiSampperRampQTampPSampQTmulti7QRampToffsetRampQTampPTampPTampNSTinterval8NQTintervalNQTintervalPRampRSamp9QamplitudeNRTintervalNPSinterval10PQampSampPonset11RampNSTintervalPamp12PSmultiPSmultiNRRinterval13NPQintervalQRampQampper14QSampRTampPQamp15SampTampperNQRintervalTamp16—SampperNPQintervalSTmultiNRRintervalRemnantSTampPTampNRSintervalNRTintervalNQTintervalPSampPSampPSintervalPSintervalNPSintervalPTmulti


#### 4.1.3. Privacy-Preserving Authentication

We now compare our two feature selection methods: a correlation coefficient method and a genetic algorithm-based method. The results indicate that the genetic algorithm-based method can find more privacy-sensitive fiducial points than the other methods. Therefore, we propose the use of a genetic algorithm for constructing privacy-preserving authentication by selecting the appropriate features. In Table6, fiducial points are listed in order according to their sensitivity to private information; the most privacy-sensitive features are at the top of the list. From this table, we select multiple fiducial points in Remnant, which are the least privacy-sensitive features. We can also select the appropriate features by excluding the top-ranked (privacy-sensitive) fiducial points.


### 4.2. Evaluation

In this subsection, we evaluate the performance of the proposed privacy-preserving authentication scheme. As previous ECG authentication schemes used on average 9 to 10 fiducial points as authentication features, we conducted experiments with 9 fiducial points as well. The fiducial points used in these experiments were selected by a genetic algorithm.For the performance evaluation, we divided our database into two parts to validate the effectiveness of the proposed authentication scheme using the selected fiducial points. In the experiment, the first part of the database was used to perform authentication based on previous works that used fiducial points includingRRinterval, PRinterval, QRinterval, RTinterval, STinterval, QTinterval, PTamp, PSamp, and PSmulti. We used the other part of the database for our evaluation. By doing so, we compared the proposed scheme that uses the newly selected fiducial points with existing authentication schemes in terms of privacy leakage.We employed the four classification models constructed in the previous section to evaluate authentication accuracy. The experimental results are presented in Tables7 and 8. In the experiment, we used two datasets: the first was generated with privacy-sensitive fiducial points, referred to as “Sense” in Table 8, and the second wasgenerated with privacy-insensitive fiducial points, referred to as “Insense” in the table. The results show that the proposed authentication scheme achieves high authentication accuracy, almost the same as previous schemes, while also preventing privacy leakage.Table 7
Evaluation results for the proposed authentication scheme.
Existing fiducial pointsNewly fiducial pointsTraining (%)Test (%)Training (%)Test (%)Naive bayes86.7586.0584.8885.26Decision tree98.9291.199.2991.44Random forest10094.7210094.64CNN99.8593.493.3693.38Table 8
Experimental results.
Gender accuracyAge accuracyConfusion matrixTrain (%)Test (%)Train (%)Test (%)T.P (%)T.N (%)D.T “Insens”84.275.775.667.283.561.6D.T “Sens”93.186.495.181.390.679R.F “Insens”96.180.683.271.787.169R.F “Sens”10090.998.985.794.384.9CNN “Insens”86.281.27871.196.8—CNN “Sens”93.288.686.579.496.8—


## 4.1. The Proposed Scheme

The overall procedure to construct the proposed privacy-preserving authentication scheme is illustrated in Figure3. As a first step in building the proposed scheme, we look for fiducial points that are the least sensitive to privacy leakage by reorganizing our database. Electrocardiogram intervals are one of the fiducial points that are essential for personal identification, but they also contain sufficient private information to classify diseases [42, 43]. Instead of using such fiducial points, we find eight new fiducial points (i.e., the 37-th ∼45-th fiducial points listed in Table 2), which eliminate the possibility of information leakage. We could figure out such fiducial points successfully by dividing existing interval values by the “R” peak point and replacing this value with the original P-QRS complex-T interval for personal authentication.Figure 3
Constructing a privacy-preserving authentication scheme.Moreover, we also determine the optimal fiducial points for privacy-preserving authentication using correlation coefficients and a genetic algorithm. In order to evaluate the quality of our proposed system, we disclose the parameters used for our algorithm. We adjust hyperparameters used for the genetic algorithm: population size, maximum replication, mutation rate, and the probability of fitness individuals. Table5 shows the parameters we used.Table 5
The initial values for parameters on the genetic algorithm.
ParameterValuesPopulation400Crossover probability0.5Crossover independent0.8Mutation probability0.01Mutation probability0.01Tournament size3


### 4.1.1. Correlation Coefficient Method

First, we use Pearson’s correlation to search forprivacy-sensitive fiducial points, which include privacy-related information, such as gender, age, and disease status. As a result of the Pearson correlation-based search, we found that fiducial points “R” and “Q” are sensitive to gender, while points “S” and “T” are less sensitive. In terms of the information about age, the fiducial points “PR,” “RS” intervals, and “S,” “Q” amplitudes are privacy-sensitive, while the “Q,” “S,” and “T” amplitudes are less sensitive.


### 4.1.2. Genetic Algorithm-Based Method

We also apply a genetic algorithm [44, 45] to construct our privacy-preserving ECG-based authentication method. A genetic algorithm is usually used to solve optimization problems that cannot be clearly defined without complex formulas. In our study, we applied a genetic algorithm to find privacy-sensitive fiducial points. In particular, we constructed an algorithm that ranks fiducial points based on their sensitivity to private information, such as gender, age, and disease status. The algorithm proceeds in five steps, which are presented in detail below.Step 1Initial Generation. The more fiducial points are used, the more sensitive the disease-sensitive fiducial points can be analyzed, but on the contrary, it takes a lot of time to calculate. We use all 45 fiducial points described in Table 2 to analyze the impact of the fiducial points.Step 2Selection. The roulette wheel selection method is applied in this step. By applying this method, we adjust a method that maximizes the probability of selecting fiducial points with higher fitness according to privacy. However, we have always opened the possibility of extracting various fiducial points for each personal information.Step 3Crossover. The multipoint crossover method is applied in this step. There are many ways to find the optimal fiducial points for each privacy information. However, we set the parental trait to be properly inherited by the offspring by using the multipoint crossover method in the crossover process.Step 4Mutation. The uniform mutation method is applied in this step. In general, the uniform mutation reduces the accuracy of fitness fiducial points for each privacy information. However, the mutation process generates properties that are not present in the parent solution, maximizing the effectiveness of the global search ability.Step 5Replacement. In this step, the algorithm compares the newly generated offspring with the parents and substitutes the low-fitness fiducial points for each privacy information.The steps 2–5 described above are performed once in a single iteration, and the iteration is repeated 400 times for each privacy information. As a result of the algorithm, all the fiducial points are listed in order according to their privacy sensitivity. Then, we remove the top-most ranked fiducial points, as they are not appropriate for authentication features in our privacy-preserving authentication scheme. This process is repeated until all the fiducial points are cleared for each privacy information.Table6 presents the results of the feature selection using our genetic algorithm. The fiducial points which are privacy-insensitive are listed in “Remnant” in the table. The results show that the fiducial point “R” is highly sensitive to gender, while the “PS-complex”values are relatively less sensitive to private information. With regards to age, fiducial points “T” and “RS-complex” are sensitive, while “PS” and “PT” are less sensitive. Fiducial point “T” is highly sensitive to disease, while “R & Q” are less sensitive to this information. Our findings regarding the disease status are supported by other works, in which methods for diagnosing diseases such as myocardial infraction [46] and unstable angina [47] were proposed using the fiducial point “T.” Our results imply that any ECG-based authentication scheme that uses privacy-sensitive fiducial points is vulnerable to privacy leakage attacks.Table 6
Feature selection results using the genetic algorithm.
IterationGenderAgeDisease1PampRSampQampNQRintervalQTampTampNRSintervalSTampToffsetNRTintervalNRSintervalSTmulti2TampPQampPSintervalPonsetNPQintervalTampperRSampNPSintervalSampperPTmultiRTmultiNRRintervalQSamp3RTampPampPSmultiQampQampPTmultiTampperPonset4QTmultiPRampSTampNPRintervalQTmultiRTmultiQampper5ToffsetNQRintervalQRampSampQSampRTamp6RTmultiSampperRampQTampPSampQTmulti7QRampToffsetRampQTampPTampPTampNSTinterval8NQTintervalNQTintervalPRampRSamp9QamplitudeNRTintervalNPSinterval10PQampSampPonset11RampNSTintervalPamp12PSmultiPSmultiNRRinterval13NPQintervalQRampQampper14QSampRTampPQamp15SampTampperNQRintervalTamp16—SampperNPQintervalSTmultiNRRintervalRemnantSTampPTampNRSintervalNRTintervalNQTintervalPSampPSampPSintervalPSintervalNPSintervalPTmulti


### 4.1.3. Privacy-Preserving Authentication

We now compare our two feature selection methods: a correlation coefficient method and a genetic algorithm-based method. The results indicate that the genetic algorithm-based method can find more privacy-sensitive fiducial points than the other methods. Therefore, we propose the use of a genetic algorithm for constructing privacy-preserving authentication by selecting the appropriate features. In Table6, fiducial points are listed in order according to their sensitivity to private information; the most privacy-sensitive features are at the top of the list. From this table, we select multiple fiducial points in Remnant, which are the least privacy-sensitive features. We can also select the appropriate features by excluding the top-ranked (privacy-sensitive) fiducial points.


## 4.1.1. Correlation Coefficient Method

First, we use Pearson’s correlation to search forprivacy-sensitive fiducial points, which include privacy-related information, such as gender, age, and disease status. As a result of the Pearson correlation-based search, we found that fiducial points “R” and “Q” are sensitive to gender, while points “S” and “T” are less sensitive. In terms of the information about age, the fiducial points “PR,” “RS” intervals, and “S,” “Q” amplitudes are privacy-sensitive, while the “Q,” “S,” and “T” amplitudes are less sensitive.


## 4.1.2. Genetic Algorithm-Based Method

We also apply a genetic algorithm [44, 45] to construct our privacy-preserving ECG-based authentication method. A genetic algorithm is usually used to solve optimization problems that cannot be clearly defined without complex formulas. In our study, we applied a genetic algorithm to find privacy-sensitive fiducial points. In particular, we constructed an algorithm that ranks fiducial points based on their sensitivity to private information, such as gender, age, and disease status. The algorithm proceeds in five steps, which are presented in detail below.Step 1Initial Generation. The more fiducial points are used, the more sensitive the disease-sensitive fiducial points can be analyzed, but on the contrary, it takes a lot of time to calculate. We use all 45 fiducial points described in Table 2 to analyze the impact of the fiducial points.Step 2Selection. The roulette wheel selection method is applied in this step. By applying this method, we adjust a method that maximizes the probability of selecting fiducial points with higher fitness according to privacy. However, we have always opened the possibility of extracting various fiducial points for each personal information.Step 3Crossover. The multipoint crossover method is applied in this step. There are many ways to find the optimal fiducial points for each privacy information. However, we set the parental trait to be properly inherited by the offspring by using the multipoint crossover method in the crossover process.Step 4Mutation. The uniform mutation method is applied in this step. In general, the uniform mutation reduces the accuracy of fitness fiducial points for each privacy information. However, the mutation process generates properties that are not present in the parent solution, maximizing the effectiveness of the global search ability.Step 5Replacement. In this step, the algorithm compares the newly generated offspring with the parents and substitutes the low-fitness fiducial points for each privacy information.The steps 2–5 described above are performed once in a single iteration, and the iteration is repeated 400 times for each privacy information. As a result of the algorithm, all the fiducial points are listed in order according to their privacy sensitivity. Then, we remove the top-most ranked fiducial points, as they are not appropriate for authentication features in our privacy-preserving authentication scheme. This process is repeated until all the fiducial points are cleared for each privacy information.Table6 presents the results of the feature selection using our genetic algorithm. The fiducial points which are privacy-insensitive are listed in “Remnant” in the table. The results show that the fiducial point “R” is highly sensitive to gender, while the “PS-complex”values are relatively less sensitive to private information. With regards to age, fiducial points “T” and “RS-complex” are sensitive, while “PS” and “PT” are less sensitive. Fiducial point “T” is highly sensitive to disease, while “R & Q” are less sensitive to this information. Our findings regarding the disease status are supported by other works, in which methods for diagnosing diseases such as myocardial infraction [46] and unstable angina [47] were proposed using the fiducial point “T.” Our results imply that any ECG-based authentication scheme that uses privacy-sensitive fiducial points is vulnerable to privacy leakage attacks.Table 6
Feature selection results using the genetic algorithm.
IterationGenderAgeDisease1PampRSampQampNQRintervalQTampTampNRSintervalSTampToffsetNRTintervalNRSintervalSTmulti2TampPQampPSintervalPonsetNPQintervalTampperRSampNPSintervalSampperPTmultiRTmultiNRRintervalQSamp3RTampPampPSmultiQampQampPTmultiTampperPonset4QTmultiPRampSTampNPRintervalQTmultiRTmultiQampper5ToffsetNQRintervalQRampSampQSampRTamp6RTmultiSampperRampQTampPSampQTmulti7QRampToffsetRampQTampPTampPTampNSTinterval8NQTintervalNQTintervalPRampRSamp9QamplitudeNRTintervalNPSinterval10PQampSampPonset11RampNSTintervalPamp12PSmultiPSmultiNRRinterval13NPQintervalQRampQampper14QSampRTampPQamp15SampTampperNQRintervalTamp16—SampperNPQintervalSTmultiNRRintervalRemnantSTampPTampNRSintervalNRTintervalNQTintervalPSampPSampPSintervalPSintervalNPSintervalPTmulti


## 4.1.3. Privacy-Preserving Authentication

We now compare our two feature selection methods: a correlation coefficient method and a genetic algorithm-based method. The results indicate that the genetic algorithm-based method can find more privacy-sensitive fiducial points than the other methods. Therefore, we propose the use of a genetic algorithm for constructing privacy-preserving authentication by selecting the appropriate features. In Table6, fiducial points are listed in order according to their sensitivity to private information; the most privacy-sensitive features are at the top of the list. From this table, we select multiple fiducial points in Remnant, which are the least privacy-sensitive features. We can also select the appropriate features by excluding the top-ranked (privacy-sensitive) fiducial points.


## 4.2. Evaluation

In this subsection, we evaluate the performance of the proposed privacy-preserving authentication scheme. As previous ECG authentication schemes used on average 9 to 10 fiducial points as authentication features, we conducted experiments with 9 fiducial points as well. The fiducial points used in these experiments were selected by a genetic algorithm.For the performance evaluation, we divided our database into two parts to validate the effectiveness of the proposed authentication scheme using the selected fiducial points. In the experiment, the first part of the database was used to perform authentication based on previous works that used fiducial points includingRRinterval, PRinterval, QRinterval, RTinterval, STinterval, QTinterval, PTamp, PSamp, and PSmulti. We used the other part of the database for our evaluation. By doing so, we compared the proposed scheme that uses the newly selected fiducial points with existing authentication schemes in terms of privacy leakage.We employed the four classification models constructed in the previous section to evaluate authentication accuracy. The experimental results are presented in Tables7 and 8. In the experiment, we used two datasets: the first was generated with privacy-sensitive fiducial points, referred to as “Sense” in Table 8, and the second wasgenerated with privacy-insensitive fiducial points, referred to as “Insense” in the table. The results show that the proposed authentication scheme achieves high authentication accuracy, almost the same as previous schemes, while also preventing privacy leakage.Table 7
Evaluation results for the proposed authentication scheme.
Existing fiducial pointsNewly fiducial pointsTraining (%)Test (%)Training (%)Test (%)Naive bayes86.7586.0584.8885.26Decision tree98.9291.199.2991.44Random forest10094.7210094.64CNN99.8593.493.3693.38Table 8
Experimental results.
Gender accuracyAge accuracyConfusion matrixTrain (%)Test (%)Train (%)Test (%)T.P (%)T.N (%)D.T “Insens”84.275.775.667.283.561.6D.T “Sens”93.186.495.181.390.679R.F “Insens”96.180.683.271.787.169R.F “Sens”10090.998.985.794.384.9CNN “Insens”86.281.27871.196.8—CNN “Sens”93.288.686.579.496.8—


## 5. Discussion

In this section, we briefly describe the existing authentication schemes and compare them with our new authentication scheme. In addition, we consider two scenarios where flaws in the current authentication system would occur. In this scenario, we explain the importance of using our proposed privacy-preserving fiducial points. Finally, we introduce a new type of multifactor authentication system that combines biometric information and existing authentication factors.


### 5.1. Vulnerability Analysis

Biel et al. [48], Irvine et al. [49], and Kyoso and Uchiyama [50] first introduced an authentication scheme in 2001. Their studies prove that ECG signals can distinguish individuals. Since many researchers have mainly focused on techniques to authenticate individuals using the user’s ECG signal with machine learning. Recently, many papers on security threats that can occur in ECG-based authentication schemes have been published. Their research shows that malicious users can capture and store users’ ECG templates and reconstruct the ECG signals. Simon et al. [14] conduct presentation attacks on the Nymi wrist bands used to verify the user’s identity. They use hardware-based AWGs and audio players to generate synthetic ECG signals to perform ECG-based biometric attacks. They succeed in the attack, but unfortunately do not mention the countermeasure techniques and safety protocols to prevent privacy leakage. Karimian et al. [15] criticize the social myth that ECG is difficult and impractical to copy, clone, or spoof through experiments. They propose a new type of attack that maps the attacker’s ECG template to the victim’s ECG template by capturing a short template of the victim’s ECG through a presentation attack. Unfortunately, they fail to discuss how the user’s private information could be leaked.Lehmann and Buschek [16] recruited 20 participants to measure ECG biometrics and identify user authentication based on the collected ECG data. In that paper, they mentioned individual privacy but did not prove that personal privacy could actually be leaked. Bera et al. [17] propose a new type of biometric-based authentication scheme using passwords and biometric information. They presented a secure authentication scheme for various cyber attacks such as replay attacks and man-in-the-middle attacks. They consider the anonymity and untraceability of individuals, including various attacks, but not privacy leakage. Karimian et al. [51] demonstrate that previously captured ECG templates 370 can be exploited in practice. They conducted spoofing attacks against their new ECG database containing 310 ECG records collected from over 90 subjects, showing an 86% success rate of online access. This paper proposes two countermeasures against ECG spoofing attacks for the first time. However, they did not consider potential problems that can arise when privacy leakages occur in authentication schemes. Unlike previously published papers, we analyze the effect of fiducial points used for actual user authentication on personal privacy from the viewpoint of privacy leakage and summarize the characteristics of each paper in Table 9.Table 9
Analysis and comparison of authentication schemes based on fiducial points.
AuthorPublicationVulnerabilityCountermeasurePrivacy leakageEberz et al.2017Presentation attackXXKarimian et al.2017Presentation attackXXLehmann et al.2020Privacy ImplicationXXBera et al.2020Replay attack etcOXKarimian et al.2020Spoofing attackOXOur paper—Privacy leakageOOIn summary, unlike previous studies, our paper demonstrates how exposure of selected fiducial points stored in the database to malicious users will not lead to additional information leakage from the users.


### 5.2. Privacy Analysis

During authentication, the system either accepts or rejects users by comparing the input data to a value stored in the database. In Section4, we propose a privacy-preserving authentication scheme. We describe three metrics that evaluate the accuracy of an authentication system.(i)
False Acceptance Rate (FAR). The likelihood that the authentication system will identify malicious and unauthorized users as legitimate users.(ii)
False Rejection Rate (FRR). The likelihood that the authentication system will identify legitimate users as malicious and unauthorized users.(iii)
Authentication Accuracy. The probability of the system to correctly identify legitimate and unauthorized users.Based on the above, we propose a privacy analysis of our privacy-preserving authentication scheme.Case 1.
When the authentication system certifies a fake user as a legitimate user. In this case, a serious problem occurs because an authentication system distinguishes a fake user as a legitimate user. The database linked with the authentication system stores fiducial points extracted from the ECG for user authentication. If fake users gain access to a system, then it means that the fake users now have the data of a victim. With acquired data, attackers can also have access to victims’ private information as the data is closely related to their personal health status. If an authentication system uses fiducial points that do not closely influence personal information, fake users can classify the victim’s sensitive information with low accuracy, even with the information obtained by breaking the system.Case 2.
When the authentication system rejects a legitimate user. This is a problem caused by the authentication system not properly identifying legitimately registered users. This is a problem of authentication accuracy, not privacy leakage, because it is not about malicious third parties obtaining information that they should not be able to.
In summary, the authentication system uses fiducial points extracted from the user’s ECG stored in the database to identify the users. However, if an authentication system identifies fake users as legitimate users, then they can conclude with some actual personal information with the ECG data that passed through the system. Once again, when a FAR error occurs in the authentication system, malicious users may use fiducial points used for authentication to identify sensitive private information that is not permitted to them due to the system error. In preparation for this flaw in the system, we should develop a privacy-preserving authentication scheme, so the fiducial points stored in the database are not directly related to the personal information of the users.


### 5.3. Multifactor Authentcation

There are three common factors used for authentication: something you know, something you have, and something you are. Each authentication factor has its own weaknesses. If users use only a single authentication factor, an authentication scheme could not guarantee sufficient security. Authentication schemes based on human knowledge, such as passwords and PINs, have low entropy [52, 53] and are vulnerable to dictionary attacks or brute force attacks. Authentication schemes based on things that people directly have, such as smart cards, may be lost or broken owing to the user’s negligence. Authentication schemes using personal biometric information are safer compared to the existing authentication factors. However, biometric authentication schemes cannot update or replace a person’s biometric information. Therefore, they can cause fatal security problems when exposed to a third party or malicious users. Overall, the authentication system could easily be broken when people apply a single authentication scheme.In order to solve this problem, research on a new type of authentication scheme combining existing authentication factors has been recently proposed. In particular, many studies have been conducted combining biometric authentication with passwords or smart cards. There have been quite a number of new types of biometric authentication schemes that are different from the existing ones, and they provide a high level of security. Jiang et al. [54] studied a secure remote control capability system of autonomous vehicles (AVs) for the safety of passengers and pedestrians. They suggest a three-step protocol that integrates passwords, biometric authentication, and smart cards to ensure secure access to autonomous vehicles.Tang et al. [55] propose an efficient Authenticated Key Agreement Scheme for secure communication in Body Area Networks (BAN). They use biometrics collected using the user’s smartphone to authenticate the user and demonstrate under the decisional bilinear Diffie-Hellman assumption to negotiate a session key to the measurement sensor. Li and Hwang [56] propose a remote user authentication scheme using smart cards and biometric information. They use the user’s biometric information to propose reliable user authentication schemes, including a nonrepudiation function. However, their authentication scheme is vulnerable to man-in-the-middle attacks due to flaws in their initial design, which does not provide secure mutual authentication between users [57].Author An [58] analyzes the authentication scheme of Das which is the previous study. Author He proposes a biometric-based remote user authentication scheme using a smart card from a security point of view and suggests an improved scheme from vulnerabilities. However, an authentication scheme proposed by An has been proven to be vulnerable to DoS attacks and forgery attacks by fellow authors in this field. Li et al. [59] mention the security vulnerabilities of the authentication scheme proposed by An. They propose a secure authentication system by combining ECC and biometric information to provide a higher level of security.In summary, when single-factor authentication techniques are applied, each authentication factor poses several unique security threats. In addition, if we simply integrate and apply multifactor authentication without considering the specificity of each authentication, the authentication schemes can retain serious vulnerabilities [60]. Therefore, it is important to impose the strengths of each authentication factor and also to simultaneously solve the weaknesses of each authentication factor [61].


## 5.1. Vulnerability Analysis

Biel et al. [48], Irvine et al. [49], and Kyoso and Uchiyama [50] first introduced an authentication scheme in 2001. Their studies prove that ECG signals can distinguish individuals. Since many researchers have mainly focused on techniques to authenticate individuals using the user’s ECG signal with machine learning. Recently, many papers on security threats that can occur in ECG-based authentication schemes have been published. Their research shows that malicious users can capture and store users’ ECG templates and reconstruct the ECG signals. Simon et al. [14] conduct presentation attacks on the Nymi wrist bands used to verify the user’s identity. They use hardware-based AWGs and audio players to generate synthetic ECG signals to perform ECG-based biometric attacks. They succeed in the attack, but unfortunately do not mention the countermeasure techniques and safety protocols to prevent privacy leakage. Karimian et al. [15] criticize the social myth that ECG is difficult and impractical to copy, clone, or spoof through experiments. They propose a new type of attack that maps the attacker’s ECG template to the victim’s ECG template by capturing a short template of the victim’s ECG through a presentation attack. Unfortunately, they fail to discuss how the user’s private information could be leaked.Lehmann and Buschek [16] recruited 20 participants to measure ECG biometrics and identify user authentication based on the collected ECG data. In that paper, they mentioned individual privacy but did not prove that personal privacy could actually be leaked. Bera et al. [17] propose a new type of biometric-based authentication scheme using passwords and biometric information. They presented a secure authentication scheme for various cyber attacks such as replay attacks and man-in-the-middle attacks. They consider the anonymity and untraceability of individuals, including various attacks, but not privacy leakage. Karimian et al. [51] demonstrate that previously captured ECG templates 370 can be exploited in practice. They conducted spoofing attacks against their new ECG database containing 310 ECG records collected from over 90 subjects, showing an 86% success rate of online access. This paper proposes two countermeasures against ECG spoofing attacks for the first time. However, they did not consider potential problems that can arise when privacy leakages occur in authentication schemes. Unlike previously published papers, we analyze the effect of fiducial points used for actual user authentication on personal privacy from the viewpoint of privacy leakage and summarize the characteristics of each paper in Table 9.Table 9
Analysis and comparison of authentication schemes based on fiducial points.
AuthorPublicationVulnerabilityCountermeasurePrivacy leakageEberz et al.2017Presentation attackXXKarimian et al.2017Presentation attackXXLehmann et al.2020Privacy ImplicationXXBera et al.2020Replay attack etcOXKarimian et al.2020Spoofing attackOXOur paper—Privacy leakageOOIn summary, unlike previous studies, our paper demonstrates how exposure of selected fiducial points stored in the database to malicious users will not lead to additional information leakage from the users.


## 5.2. Privacy Analysis

During authentication, the system either accepts or rejects users by comparing the input data to a value stored in the database. In Section4, we propose a privacy-preserving authentication scheme. We describe three metrics that evaluate the accuracy of an authentication system.(i)
False Acceptance Rate (FAR). The likelihood that the authentication system will identify malicious and unauthorized users as legitimate users.(ii)
False Rejection Rate (FRR). The likelihood that the authentication system will identify legitimate users as malicious and unauthorized users.(iii)
Authentication Accuracy. The probability of the system to correctly identify legitimate and unauthorized users.Based on the above, we propose a privacy analysis of our privacy-preserving authentication scheme.Case 1.
When the authentication system certifies a fake user as a legitimate user. In this case, a serious problem occurs because an authentication system distinguishes a fake user as a legitimate user. The database linked with the authentication system stores fiducial points extracted from the ECG for user authentication. If fake users gain access to a system, then it means that the fake users now have the data of a victim. With acquired data, attackers can also have access to victims’ private information as the data is closely related to their personal health status. If an authentication system uses fiducial points that do not closely influence personal information, fake users can classify the victim’s sensitive information with low accuracy, even with the information obtained by breaking the system.Case 2.
When the authentication system rejects a legitimate user. This is a problem caused by the authentication system not properly identifying legitimately registered users. This is a problem of authentication accuracy, not privacy leakage, because it is not about malicious third parties obtaining information that they should not be able to.
In summary, the authentication system uses fiducial points extracted from the user’s ECG stored in the database to identify the users. However, if an authentication system identifies fake users as legitimate users, then they can conclude with some actual personal information with the ECG data that passed through the system. Once again, when a FAR error occurs in the authentication system, malicious users may use fiducial points used for authentication to identify sensitive private information that is not permitted to them due to the system error. In preparation for this flaw in the system, we should develop a privacy-preserving authentication scheme, so the fiducial points stored in the database are not directly related to the personal information of the users.


## 5.3. Multifactor Authentcation

There are three common factors used for authentication: something you know, something you have, and something you are. Each authentication factor has its own weaknesses. If users use only a single authentication factor, an authentication scheme could not guarantee sufficient security. Authentication schemes based on human knowledge, such as passwords and PINs, have low entropy [52, 53] and are vulnerable to dictionary attacks or brute force attacks. Authentication schemes based on things that people directly have, such as smart cards, may be lost or broken owing to the user’s negligence. Authentication schemes using personal biometric information are safer compared to the existing authentication factors. However, biometric authentication schemes cannot update or replace a person’s biometric information. Therefore, they can cause fatal security problems when exposed to a third party or malicious users. Overall, the authentication system could easily be broken when people apply a single authentication scheme.In order to solve this problem, research on a new type of authentication scheme combining existing authentication factors has been recently proposed. In particular, many studies have been conducted combining biometric authentication with passwords or smart cards. There have been quite a number of new types of biometric authentication schemes that are different from the existing ones, and they provide a high level of security. Jiang et al. [54] studied a secure remote control capability system of autonomous vehicles (AVs) for the safety of passengers and pedestrians. They suggest a three-step protocol that integrates passwords, biometric authentication, and smart cards to ensure secure access to autonomous vehicles.Tang et al. [55] propose an efficient Authenticated Key Agreement Scheme for secure communication in Body Area Networks (BAN). They use biometrics collected using the user’s smartphone to authenticate the user and demonstrate under the decisional bilinear Diffie-Hellman assumption to negotiate a session key to the measurement sensor. Li and Hwang [56] propose a remote user authentication scheme using smart cards and biometric information. They use the user’s biometric information to propose reliable user authentication schemes, including a nonrepudiation function. However, their authentication scheme is vulnerable to man-in-the-middle attacks due to flaws in their initial design, which does not provide secure mutual authentication between users [57].Author An [58] analyzes the authentication scheme of Das which is the previous study. Author He proposes a biometric-based remote user authentication scheme using a smart card from a security point of view and suggests an improved scheme from vulnerabilities. However, an authentication scheme proposed by An has been proven to be vulnerable to DoS attacks and forgery attacks by fellow authors in this field. Li et al. [59] mention the security vulnerabilities of the authentication scheme proposed by An. They propose a secure authentication system by combining ECC and biometric information to provide a higher level of security.In summary, when single-factor authentication techniques are applied, each authentication factor poses several unique security threats. In addition, if we simply integrate and apply multifactor authentication without considering the specificity of each authentication, the authentication schemes can retain serious vulnerabilities [60]. Therefore, it is important to impose the strengths of each authentication factor and also to simultaneously solve the weaknesses of each authentication factor [61].


## 6. Conclusion

ECG-based personal authentication is being actively used nowadays in various applications. Nowadays, most ECG-based authentication schemes use a large number of fiducial points or complex algorithms to achieve high authentication accuracy. Although ECG-based authentication is considered to be more beneficial than other biometric schemes, it may cause serious privacy leakage problems because ECGs contain sufficient data to infer private information. Hence, we need to analyze the existing authentication schemes in the scope of privacy leakage.In this study, we extensively analyzed possible privacy leakages from ECG-based authentication schemes previously proposed in the literature. By employing a variety of machine learning and deep learning-based classification models, we found that most existing authentication schemes suffer from security problems that may lead to a leakage of private information, such as age, gender, and disease status. In fact, we were able to classify private information from 86% to a maximum of 98% with fiducial points used in existing ECG authentication schemes. This indicates a significant potential privacy leakage problem, and thus, we have conducted research on an authentication scheme that prevents privacy leakage.We used a genetic algorithm to classify sensitive fiducial points for each privacy, such as age, gender, and heart disease. We have also analyzed ECG data to find privacy-insensitive fiducial points, meaning that they could still be used for ECG authentication with high accuracy without containing any private information. When performing user authentication using our proposed fiducial points, our scheme can be identified with an accuracy of at least 85% or better. In terms of privacy leakage, our fiducial points have resulted in a significant decrease in private information classification accuracy; the accuracy has dropped by 9.5%, 12%, and 16.5% for gender, age, and heart disease, respectively. Based on these features, we proposed a privacy-preserving ECG-based authentication scheme.



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*Source: 7997509-2022-09-29.xml*