Cancelable biometrics is a powerful remedy for information leakage caused by the extensive usage of unprotected biometric data. Current measures usually suffer from deteriorated accuracy, which is known as the security–performance tradeoff. Motivated by these concerns, in this article, a novel cancelable fingerprint approach, i.e., Indexing-Min–Max (IMM) hashing, is proposed to securely transform a fixed-length fingerprint feature vector to a discrete index hashed code. IMM hashing is essentially established upon the min–max hash and further strengthened by the integration of the partial Hadamard transform, which alleviates performance deterioration while maintaining a high security level. Extensive experiments on FVC2002 and FVC2004 fingerprint datasets coupled with comprehensive theoretical analyses demonstrate the favorable accuracy and strong anti-attack resilience of the proposed method. Besides, compared to the unprotected counterpart, the matching precision of the protected templates yields little accuracy loss or even improved performance, which means the security–performance tradeoff is well handled. Furthermore, IMM hashing also meets the unlinkability and revocability requisites of cancelable biometrics.

With the development of cryptography, the attribute-based encryption (ABE) draws widespread attention of the researchers in recent years. The ABE scheme, which belongs to the public key encryption mechanism, takes attributes as public key and associates them with the ciphertext or the user’s secret key. It is an efficient way to solve open problems in access control scenarios, for example, how to provide data confidentiality and expressive access control at the same time. In this paper, we survey the basic ABE scheme and its two variants: the key-policy ABE (KP-ABE) scheme and the ciphertext-policy ABE (CP-ABE) scheme. We also pay attention to other researches relating to the ABE schemes, including multiauthority, user/attribute revocation, accountability, and proxy reencryption, with an extensive comparison of their functionality and performance. Finally, possible future works and some conclusions are pointed out.

In earlier multi‐receiver signcryption schemes, in order to help the authorised receivers to find the corresponding information for decryption from the ciphertext, the list of identities of all authorised receivers should be included in the ciphertext as a necessary element. However, this method has directly led to the privacy leakage problem of receivers. Later, several anonymous multi‐receiver signcryption schemes use the Lagrange interpolation method to mix and hide the real identities of all receivers, so that the ciphertext does not expose identities of the receivers any longer. Therefore the Lagrange interpolation method is considered to be a perfect solution to the receiver anonymity problem, and multiple anonymous multi‐receiver signcryption schemes have been proposed based on it. However, recently, this method has been proven to be insecure because it cannot assure the anonymity of receivers either. In fact, in any of the existing anonymous schemes based on the Lagrange interpolation method, it is easy for one authorised receiver to judge whether anyone is an authorised receiver or not, which badly violates the receiver anonymity. Motivated by these concerns, the authors propose a new multi‐receiver signcryption scheme to solve this problem. Analyses show that the proposed scheme meets confidentiality, unforgeablity and the sender and receivers’ anonymity.

Representing fingerprint templates in binary form can provide outstanding merits compared to the conventional minutiae-based fingerprint recognition system. The existing fixed-length fingerprint feature extraction methods either suffer from redundant feature magnitude or lack of template security. In this letter, we present a compact (128 bytes) and cancelable fingerprint binary codes generation scheme which enables accurate and efficient comparison as well as high security. This binary representation is also available for advanced encryption schemes (e.g., fuzzy commitment). Specifically, a kernel learning-based real-valued fingerprint feature is converted into compact and cancelable binary code via one permutation hashing. A partial Haar transform is deployed to further strengthen the irreversibility of the whole system. Experimental results on six benchmark datasets FVC2002 and FVC2004 coupled with security analysis demonstrate the superiority of the proposed method compared with several state-of-the-arts.