Multimodal biometric authentication for mobile edge computing

Sarier N. D.

INFORMATION SCIENCES, cilt.573, ss.82-99, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 573
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.ins.2021.05.036
  • Dergi Adı: INFORMATION SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, INSPEC, Library, Information Science & Technology Abstracts (LISTA), Metadex, MLA - Modern Language Association Database, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.82-99
  • Anahtar Kelimeler: Mobile Edge Computing (MEC), Multimodal Biometrics, Hill Climbing Attacks (HCA), Euclidean distance, Malicious security, Privacy Preserving Biometric Authentication (PPBA)
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) sys-tem designed for Mobile Edge Computing (MEC) and multimodal biometrics. We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user's secret parame-ters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead. Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing. (c) 2021 Elsevier Inc. All rights reserved.