An Efficient Biometric Identification in Cloud Computing With Enhanced Privacy Security

Biometric identification has developed rapidly in recent years because of its convenience and reliability. Due to the sensitivity of biometric data, many privacy-preserving biometric identification schemes have been put forward, exploiting either homomorphic encryption or matrix-transformation. However, existing schemes based on homomorphic encryption generally suffer from low computational efficiency, and existing matrix-transformation-based schemes are insufficiently secure. In this paper, we demonstrate that the matrix-transformation-based privacy-preserving biometric identification scheme recently proposed by Zhu et al. is vulnerable to a known-plaintext attack (KPA). To remedy this security flaw, we propose a new privacy-preserving biometric identification scheme, in which the property of the orthogonal matrix and additional randomness are utilized. Security analysis and comparisons indicate that our scheme can resist not only the KPA attack but also the more powerful chosen-plaintext attack (CPA), which is a reasonable attack in practice. Moreover, our scheme enjoys higher computational efficiency than other similar schemes, which implies our scheme can better support a huge database for practical biometric identification, and it also enhances privacy security of sensitive biometric data.