Topic: AI-based Privacy-preserving Biometric Authentication
A Special Issue of Journal of Surveillance, Security and Safety
ISSN 2694-1015 (Online)
Submission deadline: 31 Mar 2024
Special Issue Introduction
Biometric authentication, which relies on unique biological and behavioral characteristics, has transformed the way we access our devices, secure our data, and even enter physical spaces. However, the use of biometrics also raises important questions about privacy and security. The proliferation of biometric data, if not properly managed, could lead to potential breaches and misuse, making it imperative to integrate privacy-preserving techniques into these systems.
AI has emerged as a key enabler in addressing these challenges. AI algorithms can enhance the accuracy and reliability of biometric authentication while simultaneously ensuring the privacy of individuals by employing cryptographic techniques, secure protocols, and data anonymization methods. This Special Issue gathers contributions from experts in the fields of AI, biometrics, cybersecurity, and privacy to present the latest advancements, innovations, and best practices in AI-based privacy-preserving biometric authentication.
Topics of interest include but are not limited to novel AI-driven biometric modalities, secure storage and transmission of biometric data, ethical considerations, regulatory compliance, and real-world applications in finance, healthcare, and beyond. By fostering a deeper understanding of the challenges and opportunities in AI-based privacy-preserving biometric authentication, this Special Issue aims to contribute to the ongoing discourse on securing the digital identity landscape in an era defined by innovation and privacy concerns.
Some key topics on "AI-based Privacy-preserving Biometric Authentication" may cover:
1. Novel Biometric Modalities: Exploration of emerging biometric authentication methods, such as gait recognition, keystroke dynamics, and earprints, that leverage AI for improved accuracy and privacy;
2. Cryptographic Methods: This defines the usage of privacy-aware methods that are relevant to biometrics, including Homomorphic Encryption (HE), Zero-Knowledge Proofs (ZKPs), MPC (Multiparty Computation), Accumulators, and so on;
3. Trust Infrastructures: This outlines the mechanisms, protocols, and/or techniques used to create a trusted biometric processing infrastructure, including areas of distributed key generation (DKG), consensus mechanisms, and distributed signing;
4. Side-channel Analysis: This outlines areas where an AI-based Privacy-aware infrastructure can leak sensitive information related to Personally Identifiable Information (PII);
5. Privacy-enhancing AI Techniques: The application of advanced AI techniques, such as federated learning, HE, and differential privacy, to protect sensitive biometric data during enrolment, storage, and verification;
6. Ethical and Legal Considerations: Discussions on the ethical implications of biometric authentication, including consent, data ownership, and potential biases in AI algorithms. Analysis of relevant privacy laws and regulations worldwide;
7. Secure Storage and Transmission: Research on secure data storage solutions, encryption methods, and secure communication protocols to safeguard biometric templates and authentication processes from potential threats;
8. Adversarial Attacks: Investigation into potential vulnerabilities of AI-based biometric systems to adversarial attacks and the development of countermeasures to protect against such threats;
9. Biometric Template Protection: Techniques for biometric template protection, including cancellable biometrics and template transformation, to ensure that biometric data cannot be reverse-engineered from stored templates. Protecting the templates from Direct and Indirect attacks;
10. Multi-modal Biometrics: Research on the integration of multiple biometric modalities to enhance security and user experience while preserving privacy;
11. User Experience and Accessibility: Design principles and usability studies focus on ensuring that AI-based biometric systems are user-friendly, inclusive, and accessible to individuals with various needs and abilities;
12. Real-world Applications: Case studies and practical implementations of AI-based privacy-preserving biometric authentication in sectors such as finance, healthcare, smart cities, and mobile devices;
13. Future Trends: Forward-looking discussions on emerging trends, challenges, and opportunities in the field, including the potential impact of quantum computing on biometric security.
These topics represent the diverse and dynamic nature of research in AI-based privacy-preserving biometric authentication and highlight the multidisciplinary approach required to address the complex issues at the intersection of AI, biometrics, cryptography, and privacy.
For Author Instructions, please refer to https://www.oaepress.com/jsss/author_instructions
For Online Submission, please login at https://oaemesas.com/login?JournalId=jsss&SpecialIssueId=jsss230919
Submission Deadline: 31 Mar 2024
Contacts: Yoyo Bai, Assistant Editor, firstname.lastname@example.org