Despite the fact that biometric systems won many desirable advantages such as high security and convenience, biometric data are vulnerable to various attacks. Once the biometric data are compromised during any point of the authentication process, they are lost forever. In this paper, we propose a security information management approach to address privacy concerns emerging from the management of biometric data in remote Photoplethysmogram (rPPG) biometric authentication systems. In our design, Fully Homomorphic Encryption (FHE) is introduced to protect PPG signals in the transmission and storage phases. We further design an efficient FHE-based Convolutional Neural Network (CNN) model and encrypt the key model parameters of the CNN to realize secure inference. In addition, Principal Component Analysis, Max-Relevance and Min-Redundancy, and Relative Mutual Information methods are employed in the feature extraction stage to screen out the most representative PPG features, thus improving the accuracy of PPG authentication. In the experiments, we adopt eight publicly PPG/rPPG databases and our own rPPG dataset collected via a remote camera to evaluate our proposed biometric authentication system. The experimental results show that our proposed remote PPG biometric authentication system outperforms the state-of-the-art related work in terms of balancing authentication accuracy and biometric privacy protection.

Advances in the Internet of Things, such as biosensing and camera-capturing technologies, have also resulted in biometric-based authentication approaches becoming more viable. Photoplethysmography (PPG), for example, can be leveraged to provide better biometric features for continuous authentication, in comparison to other biometrics such as fingerprint. However, the fewer morphological features in PPG signals can complicate the accurate authentication of PPG signals. Furthermore, one has to also consider transmission security and PPG template storage security. These two considerations are typically not taken into consideration in most existing PPG-based authentication schemes. Therefore, we design a secure PPG-based biometric system to achieve accurate authentication with biometric privacy protection. In our design, Homomorphic Random Forest is adopted to classify the homomorphically encrypted biometric features, thus protecting the user’s PPG biometrics from being compromised in authentication. Furthermore, beat qualification screening is set up to avoid the interference of unqualified signals, and 19 features with the least redundancy from the 541 features extracted are selected as the biometric features of the user. Doing so allows us to ensure the accuracy of authentication, as demonstrated in our evaluations using five PPG databases collected by three different collection methods (contact, remote, and monitor). In addition, our experiments adopt PPG signals acquired from remote cameras, which are not considered in other PPG-based biometric systems. The experimental results show that the average accuracy of our biometric system is 96.4%, the F1 score is 96.1%, the equal error rate is 2.14%, and the authentication time is about 0.5 s.

Biometric-based authentication methods have been widely used, for example on portable devices (e.g., Android and iOS devices). However, there are several known limitations in existing authentication methods based on biometrics (e.g., those using facial, iris, and fingerprint). For example, in a healthcare context, a user may be physically incapable of completing the authentication due to his/her medical conditions. Hence, as a complementary authentication mechanism, there have been attempts to also utilize electrocardiogram (ECG). In this work, we propose an ECG authentication system that leverages deep learning. Specifically, to achieve generalization ability, complementary ensemble empirical decomposition (CEEMD) is introduced in our design. Moreover, a 1-D Multi-scale Convolutional Neural Network (1-D MCNN) is implemented to achieve accurate authentication. To evaluate the usability of our proposed approach, we have performed extensive experiments on eight databases, and the findings show that our proposed approach achieves good performance even on abnormal databases and can be adapted for different application environments. In addition, our adopted data from eight public databases requires theoretical statistical treatment for practical applications in real authentication scenarios.

The potential of using electrocardiogram (ECG), an important physiological signal for humans, as a new biometric trait has been demonstrated, and ongoing efforts have focused on utilizing deep learning (e.g., 2D neural networks) to improve authentication accuracy (with some efficiency tradeoffs). In most of the existing ECG-based authentication approaches, the ECG recordings for enrollment and testing are collected within short intervals (e.g., within an hour). However, since ECG biometrics change over time, this design may decrease authentication accuracy when ECG recordings are collected weeks or even months prior. In this article, we propose 1D Integrated EfficientNet (1DIEN) to achieve cross-session ECG authentication. We adopt 1D neural networks as a lightweight alternative to 2D neural networks, and a voting scheme is designed to reduce variance and improve general authentication performance. We use three public ECG databases (i.e., an inter-session database, a mixed-session database, and an intra-session database) to evaluate our proposed 1DIEN under different authentication scenarios. The experimental results show that our approach achieves satisfactory performance for ECG authentication at a 3-month interval and is suitable for practical applications.

Electronic health (e-health) networks enable users to enjoy convenient, flexible, and low-cost medical services at home, so they attract great attention and spread into the market quickly. In e-health networks, large amounts of various health data including personal privacy information and physiological signals are transmitted, which raises security risks. To protect the health data transmitted in e-health networks, steganography-based solutions have been widely researched. Although existing steganography-based solutions successfully hide health data in physiological signals such as electrocardiograms (ECG), forward secrecy is not fully considered. This means that adversaries are able to extract users’ health data hidden in previous stego signals by using compromised long-term secrets. Moreover, to reduce communication overhead, compression techniques are introduced in some steganography-based methods. However, the imperceptibility and embedding capacity of these solutions are sacrificed. To solve the above issues, in this study, we adopt Singular Value Decomposition (SVD) and the Bose-Chaudhuri-Hocquenghem (BCH) codes to design an efficient and secure health data propagation scheme based on steganography and compression. In our design, the BCH codes are used to update the encryption key and change the embedding locations in each steganography process, thus achieving forward secrecy and further enhancing the security of steganography. Moreover, a two-stage compression method is proposed in our scheme to compress the signals during signal processing and compression phases, which effectively reduces the communication overhead. Security analysis and the experimental results show that our proposed scheme enhances security while achieving an elaborate balance between imperceptibility, embedding capacity, and compression.

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients’ wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

Proximity testing technologies have been of increasing importance in vehicularad-hocnetworks (VANETs), especially in location-based services. However, there exist several known challenges in most existing proximity testing methods. For instance, during proximity testing, how to protect the location privacy of users, guarantee the fairness trait of both communication parties, and reduce computational costs is challenging. In this article, we present an efficient privacy-preserving proximity testing scheme using private set intersection (PSI) and differential privacy. In our design, a Chebyshev-based PSI is constructed to achieve location privacy with low energy consumption during the proximity testing process. Furthermore, geo-indistinguishability is employed in our scheme to generate virtual points as inputs set of PSI, which further protects the location privacy from exposure and provides resistance to collusion attacks. Fairness requirement is alsosatisfied in our scheme. The performance evaluation shows that the proposed scheme achieves good efficiency and is suitable for VANETs.

Electric vehicle charging is becoming more commonplace, but a number of challenges remain. For example, the wireless communications between vehicle users and aggregators can be subject to exploitation and hence, several authentication schemes have been designed to support varying levels of privacy protection. However, there are a number of limitations observed in existing authentication schemes, and examples include lack of anonymity and not considering charging peak in their design (and consequently, not meeting low energy consumption requirement in smart grid environments). More recently, there have been attempts to utilize Chevyshev chaotic map in the design of authentication mechanism, with the aims of reducing computational costs yet achieving high security. However, the security requirements of Chebyshev polynomials pose new challenges to the construction of Chebyshev chaotic maps-based authentication schemes. To solve these limitations, we propose an efficient Chebyshev polynomials algorithm by adopting a square matrix-based binary exponentiation algorithm to provide secure and efficient Chebyshev polynomial computation. We further construct an energy-efficient authentication and key negotiation scheme for the smart grid environments based on the proposed algorithm. Compared with five other competing schemes, our proposed authentication scheme achieves reduced computational and communication costs. In addition, the ProVerif tool is used to analyze the security of our proposed authentication scheme. The results show that the proposed scheme outperforms these five other schemes in terms of computation and communication overheads while achieving privacy preserving.

Telecare medicine information systems (TMIS) provide flexible and convenient e-health care. However, the medical records transmitted in TMIS are exposed to unsecured public networks, so TMIS are more vulnerable to various types of security threats and attacks. To provide privacy protection for TMIS, a secure and efficient authenticated key agreement scheme is urgently needed to protect the sensitive medical data. Recently, Mishra et al. proposed a biometrics-based authenticated key agreement scheme for TMIS by using hash function and nonce, they claimed that their scheme could eliminate the security weaknesses of Yan et al.'s scheme and provide dynamic identity protection and user anonymity. In this paper, however, we demonstrate that Mishra et al.'s scheme suffers from replay attacks, man-in-the-middle attacks and fails to provide perfect forward secrecy. To overcome the weaknesses of Mishra et al.'s scheme, we then propose a three-factor authenticated key agreement scheme to enable the patient to enjoy the remote healthcare services via TMIS with privacy protection. The chaotic map-based cryptography is employed in the proposed scheme to achieve a delicate balance of security and performance. Security analysis demonstrates that the proposed scheme resists various attacks and provides several attractive security properties. Performance evaluation shows that the proposed scheme increases efficiency in comparison with other related schemes.

The last few years have witnessed an explosive growth in the research of information hiding in multimedia objects, but few studies have taken into account packet loss in multimedia networks. As one of the most popular real-time services in the Internet, Voice over Internet Protocol (VoIP) contributes to a large part of network traffic for its advantages of real time, high flow, and low cost. So packet loss is inevitable in multimedia networks and affects the performance of VoIP communications. In this study, a fractal-based VoIP steganographic approach was proposed to realize covert VoIP communications in the presence of packet loss. In the proposed scheme, secret data to be hidden were divided into blocks after being encrypted with the block cipher, and each block of the secret data was then embedded into VoIP streaming packets. The VoIP packets went through a packet-loss system based on Gilbert model which simulates a real network situation. And a prediction model based on fractal interpolation was built to decide whether a VoIP packet was suitable for data hiding. The experimental results indicated that the speech quality degradation increased with the escalating packet-loss level. The average variance of speech quality metrics (PESQ score) between the “no-embedding” speech samples and the “with-embedding” stego-speech samples was about 0.717, and the variances narrowed with the increasing packet-loss level. Both the average PESQ scores and the SNR values of stego-speech samples and the data-retrieving rates had almost the same varying trends when the packet-loss level increased, indicating that the success rate of the fractal prediction model played an important role in the performance of covert VoIP communications.

A suitable key agreement protocol plays an essential role in protecting the communications over open channels among users using voice over Internet protocol (VoIP). This study presents a robust and flexible password authenticated key agreement protocol with user anonymity for session initiation protocol (SIP) used by VoIP communications. Security analysis demonstrates that the proposed protocol enjoys many unique properties, such as user anonymity, no password table, session key agreement, mutual authentication, password updating freely, conveniently revoking lost smartcards and so on. Furthermore, the proposed protocol can resist the replay attack, the impersonation attack, the stolen‐verifier attack, the man‐in‐middle attack, the Denning‐Sacco attack and the offline dictionary attack with or without smartcards. Finally, the performance analysis shows that the protocol is more suitable for practical application in comparison with other related protocols.

ABSTRACT Session Initiation Protocol (SIP) is one of the most commonly used protocols for handling sessions for over Internet Protocol based communications, and the security of SIP is becoming increasingly important. Recently, Zhang et al. proposed a password‐authenticated key agreement protocol for SIP by using smart cards to protect the VoIP communications between users. Their protocol provided some unique features, such as mutual authentication, no password table needed, and password updating freely. In this study, we performed cryptanalysis of Zhang et al.'s protocol and found that their protocol was vulnerable to the impersonation attack although the protocol could withstand several other attacks. A malicious attacker could compute other users' privacy keys and then impersonated the users to cheat the SIP server. Furthermore, we proposed an improved password‐authentication key agreement protocol for SIP, which overcame the weakness of Zhang et al.'s protocol and was more suitable for Voice over Internet Protocol communications. Copyright © 2014 John Wiley & Sons, Ltd.