Development of secured algorithm to enhance the privacy and security template of biometric technology

A cancelable biometric identification scheme based on bloom filter and format-preserving encryption

Biometric data, such as fingerprints, facial recognition, retina scans, and voice scans, constitute unique and irreplaceable forms of personal identification. In the digital era, biometric data is increasingly used in electronic security systems for identity verification and user authentication. However, despite these benefits, there are potential risks of privacy violations and data misuse, which impact human rights. This study aims to analyze the legal protection of biometric data in digital security systems from a criminal law perspective and examine criminal liability in the event of biometric data leaks or misuse. The research method used is a normative juridical approach, examining relevant statutory provisions, such as Law Number 27 of 2022 concerning Personal Data Protection, Law Number 11 of 2008 concerning Electronic Information and Transactions (ITE), and the Criminal Code (KUHP). The results show that although biometric data has been recognized as legally protected personal data, criminal law enforcement mechanisms for biometric data-related violations are still suboptimal. This is due to the lack of clear technical regulations and limitations regarding the definition, storage, and use of biometric data by electronic system administrators. This study concludes that strengthening criminal law regulations and implementing policies is necessary to ensure comprehensive biometric data protection, including imposing strict sanctions on perpetrators of misuse and increasing corporate responsibility in maintaining the security of the public’s digital data.

The need for fool proof authentication procedures away from traditional authentication mechanisms like passwords, security PINS has led to the advent of biometric authentication in information systems. Biometric data extracted from physiological features of a person including but not limited to fingerprints, palm prints, face or retina for purpose of verification & identification is saved as biometric templates. The inception of biometrics in access control systems has not been without its own hitches & like other systems it has its fair share of challenges. Biometric fingerprints being the most mature of all biometric spheres are the most widely adopted biometric authentication systems. Biometric systems effectiveness lies on how secure they are at preventing inadvertent disclosure of biometric templates in an information system’s archive. This however has not been the case as biometric templates have been fraudulently accessed to gain unauthorized access in identification and verification systems. In order to achieve strong and secure biometric systems, biometric systems developers need to build biometric systems that properly secure biometric templates. Several biometric template protection schemes and approaches have been proposed and used to safeguard stored biometric templates. Despite there being various biometric template protection schemes and approaches in existence, none of them has provided the most authentic, reliable, efficient and deterrent means to totally secure biometric fingerprint templates. This research sought to establish status of the current biometric template protection techniques and methods by conducting a survey and analyzing data gathered from a sample of seventy-eight (78) respondents. We will report these results and give our conclusion based on findings of the survey in this paper.

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge.

A linear convolution-based cancelable fingerprint biometric authentication system

Smartwatches like Fitbits provide users with easy access to quantifiable health data. In the sports industry, tracking this biometric information may be particularly beneficial to athletes, whose livelihoods revolve around their health and fitness. Nonetheless, under the current regime, professional and collegiate athletes’ biometric health data are inadequately protected. Data privacy law is still in its infancy, but in the meantime, athletes must consider that motivations to sell or misuse players’ biometric information may outpace legal developments. This Paper will analyze the promise and risk of collecting professional and collegiate athletes’ health and biometric data, particularly through fitness wearables. It will provide a closer look at wearables in professional sports and consider the increased risk posed to college athletes. Finally, this Paper will consider possible solutions to maximize the benefits of newfound technology while simultaneously minimizing risks to players’ health information, privacy, and personal data ownership.

Nowadays, biometric recognition represents an integral component of identity management and access control systems, replacing PINs or passwords. However, the wide deployment of biometric recognition systems in the past decades has raised numerous privacy concerns regarding the storage and use of biometric data. Due to the fact that the link between individuals and their biometric characteristics, e.g. fingerprints or iris, is strong and permanent, biometric reference data (templates) need to be protected in order to safeguard individuals' privacy and biometric systems' security. In particular, unprotected biometric templates can be abused to crossmatch biometric databases, i.e. tracking individuals without consent, and to launch presentation attacks employing specific inversion techniques. Technologies of biometric template protection offer solutions to privacy preserving biometric authentication, which improves the public confidence and acceptance of biometric systems. While biometric template protection has been an active research topic over the last 20 years, proposed solutions are still far from gaining practical acceptance. The existing gap between theory and practice, which results in a trade-off between privacy protection and recognition accuracy, is caused by numerous factors. To facilitate a standardized design and application of biometric template protection schemes, researchers have to solve several open problems. This chapter provides an overview of state-of-the-art technologies of biometric template protection and discusses main issues and challenges.

The exponential growth of social media platforms has created new avenues for communication, brand engagement, and information sharing. However, this digital expansion has also increased vulnerabilities to identity fraud, phishing, misinformation, and a wide range of cyber threats. These risks are especially critical for industries and organizations that operate under strict regulatory and compliance frameworks, including finance, healthcare, and government sectors. This paper proposes the development of compliance-oriented social media risk management models tailored to address identity fraud and cyber threats in real-time. The proposed models integrate regulatory requirements with cybersecurity best practices to create a structured approach for identifying, assessing, and mitigating risks originating from social media activities. The models utilize advanced data analytics, natural language processing (NLP), and machine learning algorithms to detect anomalies, suspicious patterns, and unauthorized access or impersonation attempts. Compliance alignment is achieved by embedding regulatory controls such as GDPR, HIPAA, and ISO/IEC 27001 into the risk assessment matrix to ensure accountability, data privacy, and operational integrity. Furthermore, the framework incorporates real-time monitoring, automated incident response, and threat intelligence integration to enhance situational awareness and reduce response latency. A modular design allows for scalability and adaptability across sectors and platforms. The effectiveness of the proposed models is demonstrated through simulation scenarios involving identity theft and brand impersonation attacks on social media. Results show improved threat detection accuracy, reduced false positives, and enhanced compliance adherence. By bridging the gap between regulatory compliance and cybersecurity resilience, the study offers a proactive solution to emerging threats in the digital communication landscape. This research underscores the importance of incorporating compliance and governance principles into cybersecurity strategies for social media, thus enabling organizations to safeguard their digital presence, protect sensitive information, and maintain public trust.

Biometrics are increasingly used for access control, fraud detection, and authentication systems. Nevertheless, attackers can deceive such systems using forged biometrics. This research proposes a novel method that makes biometric security systems more resilient to such attacks. The proposed method transforms the user’s biometric data into an irreversible code to protect the original data. This code combines data from multiple biometric modalities, making fabricating a false biometric harder. Additionally, the proposed method does not depend on any secret keys, which helps avoid cases of stolen tokens. The proposed method utilizes the generative adversarial network (GAN) to generate synthetic biometric templates from multiple modalities, which is considered a transformation function for biometric data. Three fusion levels are presented; features from multiple biometric modalities are extracted first in each fusion level. Subsequently, the features train a generative adversarial network to produce synthesized biometric templates. These synthesized templates serve as secure substitutes for the original biometrics during authentication, preventing direct exposure of raw biometric data. We evaluated our methods on the CASIA-V3-Internal and MMU1 iris datasets and the AT&T (ORL) and FERET face datasets. The results showed that our proposed methods can achieve higher accuracy, usability, and improved security compared to a single biometric modality. The proposed feature-level, GAN-based, and decision-level fusion schemes achieved 2.03%, 0.82%, and 0.0297% error rates, respectively, for CASIA and ORL datasets and 1.53%, 0.80%, and 0.0313% error rates, respectively, for MMU1 and FERET datasets. Moreover, we have demonstrated that our method resists pre-image and correlation attacks.

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network–the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Unlinkable and irreversible biometric template protection based on bloom filters

Deployments of biometric technologies are already widely disseminated, i.e. the protection of biometric reference data becomes vital in order to safeguard individuals' privacy. Biometric template protection techniques are designed to protect biometric templates in an irreversible and unlinkable manner (ISO/IEC IS 24745). In addition, these schemes are required to maintain key system properties, e.g. biometric performance or authentication speed. Recently, template protection schemes based on Bloom filters have been introduced and applied to various biometric characteristics, such as iris or face. While a Bloom filter-based representation of biometric templates is irreversible the originally proposed system has been exposed to be vulnerable to cross-matching attacks. In this paper we address this issue and demonstrate that any kind of Bloom filter-based representation of biometric templates can be transformed to an unordered set of integer values which enables a locking of irreversible templates in a fuzzy vault scheme from Dodis et al. which can be secured against known cross-matching attacks. In addition, experiments which are carried out on a publicly available iris database, show that the proposed scheme retains the biometric performance of the original system.

With the increasing development of internet, the security of personal information becomes more and more important. Thus, variety of personal recognition methods have been introduced to ensure persons' information security. Traditional recognition methods such as Personal Identification Number (PIN), or Identification tag (ID) are vulnerable to hackers. Then the biometric technology, which uses the unique physiological characteristics of human body to identify user information has been proposed. But the biometrics widely used at present such as human face, fingerprint, iris, and voice can also be forged and falsified. The biometric with living body features such as electromyography (EMG) signal is a good method to achieve aliveness detection and prevent the spoofing attacks. However, there are few studies on personal recognition based on EMG signal. According to the application context, personal recognition system may operate either in identification mode or verification mode. In the personal identification mode, the system recognizes an individual by searching the templates of all the users in the database for a match. While in the personal verification mode, the system validates a person's identity by comparing the captured features with her or his own template(s) stored in the system database. In this paper, both EMG-based personal identification method and EMG-based personal verification method are investigated. First, the Myo armband is placed on the right forearm (specifically, the height of the radiohumeral joint) of 21 subjects to collect the surface EMG signal under hand-open gesture. Then, two different methods are proposed for EMG-based personal identification, i.e., personal identification method based on Discrete Wavelet Transform (DWT) and ExtraTreesClassifier, and personal identification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN). Experiments with 21 subjects show that the identification accuracy of this two methods can achieve 99.206% and 99.203% respectively. Then based on the identification method using CWT and CNN, transfer learning algorithm is adopted to solve the model update problem when new data is added. Finally, an EMG-based personal verification method using CWT and siamese networks is proposed. Experiments show that the verification accuracy of this method can achieve 99.285%.

Biometric data forensics is a crucial intersection between forensic science and biometric technology. Biometric data forensics refers to the use of biometric data i.e. unique physical or behavioral traits of individuals, such as fingerprints, facial features, or iris patterns, to solve crimes and verify identities. Its importance stems from the ability to provide reliable, objective evidence that can aid in criminal investigations and ensure security in various applications. Unlike traditional evidence, biometric data offers a unique identifier that can corroborate identities with a high degree of certainty.

With the increasing demand for secure file-sharing mechanisms, crptographic encryption combined with QR codes has emerged as an efficient and reliable approach to safeguarding sensitive data [1]. This paper presents a secure file transfer system that employs the Fernet symmetric encryption algorithm to encrypt files and encodes the encrypted file path into a $Q R$ code for seamless sharing [2]. The system enhances security by dynamically generating cryptography keys, ensuring that unauthorized access is nearly impossible [3]. Additionally, it integrates user authentication mechanisms, adding an extra layer of security and ensuring that only legitimate users can decrypt and access the shared files [4]. Unlike traditional file-sharing methods that are prone to data breaches, phishing attacks, and unauthorized interceptions [5], this approach mitigates such vulnerabilities by eliminating the need for manual key exchanges. By automating encryption, QR code generation, and secure key management, the system minimizes human errors and makes the process both user-friendly and highly secure [6]. The adoption of AES-based encryption ensures strong resistance against bruteforce attacks [1], while the two-factor authentication (2FA) feature prevents unauthorized decryption attempts [7]. This research explores the step-by-step architecture of the system, including its workflow, encryption model, and security evaluations [2]. The experimental results demonstrate that the proposed framework provides high-speed encryption and decryption, making it feasible for real-time applications [6]. Additionally, QRbased key management significantly enhances usability and accessibility while maintaining a high level of data protection [7]. The system is cross-platform compatible, supporting Windows, mac OS, Linux, and mobile devices, ensuring broad usability in various environments [4]. By leveraging QR code technology for secure key distribution, this solution offers a lightweight yet highly robust alternative to conventional encryption-based file-sharing techniques [5]. The proposed framework not only strengthens data confidentiality and integrity but also simplifies the process of secure file transfer, making it suitable for diverse applications, including government, corporate, and personal data protection [6]. Future research may focus on scalability, integration with cloud storage, and adaptive encryption mechanisms to further enhance security and performance [].