Published January 16, 2025 | Version v1
Conference paper Open

Encrypted Biometric Search: A Deep Learning Approach to Scalable and Secure Cross-Border Data Exchange.

  • 1. Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Heraklion, Greece
  • 2. Department of Informatics and Telematics Harokopio University of Athens Athens, Greece

Description

Cross-border collaboration among Law Enforcement Agencies is essential for effective and timely suspect identification, especially when the availability of biometric data varies between agencies. This paper presents a scalable and secure approach for multimodal biometric identification across multiple jurisdictions. Our approach allows Law Enforcement Agencies to combine biometric modalities -facial images, fingerprints, and voice samples- and compare them with collaborating agencies, improving the overall accuracy and effectiveness of suspect identification. By leveraging deep learning models for indexing and comparison, efficient data retrieval was achieved without compromising privacy or security. To ensure the protection of sensitive biometric data, our approach incorporates advanced encryption mechanisms, including Homomorphic Encryption for secure computations and Advanced Encryption Standard (AES encryption) for safeguarding biometric information. Its decentralised architecture allows each Law Enforcement Agency to maintain independent instances of the Deep Learning Indexer and Comparator, minimising risks associated with centralising sensitive data and supporting seamless collaboration between agencies. This approach not only improves the accuracy of suspect identification but also enhances operational efficiency by allowing Law Enforcement Agencies to query and share biometric data securely across borders.

Files

Encrypted Biometric Search A Deep Learning Approach to Scalable and Secure Cross-Border Data Exchange..pdf

Additional details

Identifiers

ISSN
2573-2978

Funding

European Commission
TENSOR - Reliable biomeTric tEhNologies to asSist Police authorities in cOmbating terrorism and oRganized crime 101073920

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