Similarity preserving is a key ingredient of cancellable biometric scheme design. The notion ensures the accuracy performance of the biometric systems can be preserved after the cancellable biometric technique is applied. Random Projection is among the most commonly adopted method in cancellable biometric schemes. However, it is reversible subject to certain conditions, which disrupts the template irreversibility criterion. This invites vulnerabilities for random projection-based schemes. In this paper, we propose a novel transform function, namely Absolute Value Equations Transform (AVET), which non-linearly projects feature vectors to another domain. The transformed templates hold two main merits ensuring the user’s privacy, and maintaining the system’s performance simultaneously. First, by relying on the hardness of the Absolute Value Equations problem, we guarantee that AVET satisfies irreversibility. Second, by using Johnson–Lindenstrauss lemma and the inverse triangle inequality, we prove that the proposed approach has the similarity preserving property. Notably, rigorous theoretical proofs and empirical experiments are provided. The efficacy of AVET is comprehensively evaluated on both physiological and behavioral biometrics including face, ear, fingerprint, and gait. With unimodal approach, we achieve competitive performances compared to related algorithms on eight public datasets. Regarding bimodal mode, the AVET surpasses the state-of-the-art technique on all three observed datasets. To the best of our knowledge, this is the first study that attempts to develop a secure transformation to augment the role of Random Projection in the existing cancellable biometric schemes.

The goal of group testing is to efficiently identify a few specific items, called positives, in a large population of items via tests. A test is an action on a subset of items that returns positive if the subset contains at least one positive and negative otherwise. In non-adaptive group testing, all tests are independent, can be performed in parallel, and represented as a measurement matrix. In this work, we consider non-adaptive group testing with consecutive positives in which the items are linearly ordered and the positives are consecutive in that order. We present two algorithms for efficiently identifying consecutive positives. In particular, without storing measurement matrices, we can identify up to$d$consecutive positives with$2 \log_{2}\frac{\mathrm{n}}{d}+2d (4\log_{2}\frac{n}{d}+2d,\ resp.)$tests in$O(\log_{2}^{2}\frac{n}{d}+d)\ (O(\log_{2}\frac{n}{d}+d)$, resp.) time. These results significantly improve the state-of-the-art scheme in which it takes$5 \log_{2}\frac{n}{d} +2d+21$tests to identify the positives in$O(\frac{n}{d}\log_{2}\frac{n}{d}+d^{2})$time with the measurement matrices associated with the scheme stored somewhere.

Wireless Sensor Networks (WSNs) are being used more and more and are becoming a key technology in applications ranging from military ones to ones used in daily life. There are basic architectures: one comprising sensors and a server and one comprising sensors, a server, and storage nodes between them ("two-tiered architecture"). We investigate this second type as it has many advantages in terms of energy usage, computation, and data transmission. Although two-tiered wireless sensor networks have many advantages, security is a critical due to three main problems. First, sensors located in hostile areas can be surreptitiously replaced with fake ones that send bogus data. Second, an attacker could install new sensors with valid authentication keys that send bogus data to storage nodes and deceive the server. Third, a storage nodes could be compromised and reveal data received from sensors. Therefore, the server must authenticate sensors before accepting data from them, detect whether a key was intercepted and identify which one, and handle the most common queries while preserving the privacy of data received from storage nodes. W have developed a novel solution using Non-Adaptive Group Testing that enables a server to perform these tasks efficiently and effectively. This solution is secure with high probability against an attack that tries to guess sensor data and thus protects data confidentiality.

Authentication schemes using tokens or biometric modalities have been proposed to ameliorate the security strength on mobile devices. However, the existing approaches are obtrusive since the user is required to perform explicit gestures in order to be authenticated. While the gait signal captured by inertial sensors is understood to be a reliable profile for effective implicit authentication, recent studies have been conducted in ideal conditions and might therefore be inapplicable in the real mobile context. Particularly, the acquiring sensor is always fixed to a specific position and orientation. This paper mainly focuses on addressing the instability of sensor's orientation which mostly happens in the reality. A flexible solution taking advantages of available sensors on mobile devices which can help to handle this problem is presented. Moreover, a novel gait recognition method utilizes statistical analysis and supervised learning to adapt itself to the instability of the biometric gait under various circumstances is also proposed. By adopting PCA+SVM to construct the gait model, the proposed method outperformed other state-of-the-art studies, with an equal error rate of 2.45\% and accuracy rate of 99.14\% in terms of the verification and identification aspects being achieved, respectively.

RSA cryptosystem is the most popular public key cryptosystem which provides both secrecy and digital signatures. Due to RSA's popularity, many attacks on it have been developed. In this paper, we consider experimentally attacks on low private exponent RSA and find that: (i) lattice attack using Gauss lattice reduction algorithm is more effective than Wiener attack, and (ii) it is not always to recover decryption exponent even if its bit-length is less than one-quarter bit-length of the modulus. The results also raise an open question on the conditions to recover the RSA private key from public key.

Wilson introduced XCSF as a successor to XCS. The major development of XCSF is the concept of a computed prediction. The efficiency of XCSF in dealing with numerical input and continuous payoff has been demonstrated. However, the possible actions must always be determined in advance. Yet domains such as robot control require numerical actions, so that neither XCS nor XCSF with their discrete actions can yield high performance. This paper studies computed action in XCSF, where the action is continuous with respect to the input state. In comparison with Wilson's architecture for continuous action, our XCSF version, called XCSFCA, proves to be more efficient.