Modern access controls employ biometrics as a means of authentication to a great extent. For example, biometrics is used as an authentication mechanism implemented on commercial devices such as smartphones and laptops. This paper presents a fingerprint biometric cryptosystem based on the fuzzy commitment scheme and convolutional neural networks. One of its main contributions is a novel approach to automatic discretization of fingerprint texture descriptors, entirely based on a convolutional neural network, and designed to generate fixed-length templates. By converting templates into the binary domain, we developed the biometric cryptosystem that can be used in key-release systems or as a template protection mechanism in fingerprint matching biometric systems. The problem of biometric data variability is marginalized by applying the secure block-level Bose–Chaudhuri–Hocquenghem error correction codes, resistant to statistical-based attacks. The evaluation shows significant performance gains when compared to other texture-based fingerprint matching and biometric cryptosystems.

In this paper, we propose a new system for a sequential secret key agreement based on 6 performance metrics derived from asynchronously recorded EEG signals using an EMOTIV EPOC+ wireless EEG headset. Based on an extensive experiment in which 76 participants were engaged in one chosen mental task, the system was optimized and rigorously evaluated. The system was shown to reach a key agreement rate of 100%, a key extraction rate of 9%, with a leakage rate of 0.0003, and a mean block entropy per key bit of 0.9994. All generated keys passed the NIST randomness test. The system performance was almost independent of the EEG signals available to the eavesdropper who had full access to the public channel.

The main goal of this paper is the proposal of a key-based steganographic system in which the ratio of capacity and image quality metrics that represents the stego object while reducing the detectability of hidden content was improved. The main contribution of the proposed steganographic system is a new algorithm for selecting stego areas. The area selection algorithm is based on clustering the pixels of the cover object into a predetermined number of clusters. The goal of this selection of areas (clusters) is to group as many homogeneous parts of the image as possible in order to cover these areas with as few rectangular shapes as possible. Since the data on the defined rectangles represent the key of the system, the capacity of the additional secret channel is minimized in this way. On the obtained stego-carriers, an embedding of test random content is performed in order to estimate its detectability. By combining the proposed area selection method with the Minimal Decimal Difference steganographic method, a system was created with an optimal trade-off between detectability of secret content, quality and capacity of the carrier, and the length of the stego-key. Finally, a comparison of the obtained results with relevant adaptive steganographic methods is presented. The proposed concept obtains its verification in one practical system for secure file transfer of controlled cryptographic strength.

Symmetric cryptography methods have an important role in security solutions design in data protection. In that context, symmetric cryptography algorithms and pseudo-random generators connected with them have strong influence on designed security solutions. In the computationally constrained environment, security efficiency is also important. In this paper we proposed the design of a new efficient pseudo-random generator parameterized by two pseudo-random sequences. By the probabilistic, information-theoretic and number theory methods we analyze characteristics of the generator. Analysis produced several results. We derived sufficient conditions, regarding parameterizing sequences, so that the output sequence has uniform distribution. Sufficient conditions under which there is no correlation between parameterizing sequences and output sequence are also derived. Moreover, it is shown that mutual information between the output sequence and parameterizing sequences tends to zero when the generated output sequence length tends to infinity. Regarding periodicity, it is shown that, with appropriately selected parameterizing sequences, the period of the generated sequence is significantly longer than the periods of the parameterizing sequences. All this characteristics are desirable regarding security applications. The efficiency of the proposed construction can be achieved by selection parameterizing sequences from the set of efficient pseudo-random number generators, for example, multiple linear feedback shift registers.

It is well known that Renyi’s entropy of order 2 determines the maximum possible length of the distilled secret keys in sequential secret key distillation protocols so that no information is leaked to the eavesdropper. There have been no attempts to estimate this key quantity based on information available to the legitimate parties to this protocol in the literature. We propose a new machine learning system, which estimates the lower bound of conditional Renyi entropy with high accuracy, based on 13 characteristics locally measured on the side of legitimate participants. The system is based on a prediction intervals deep neural network, trained for a given source of common randomness. We experimentally evaluated this result for two different sources, namely 14 and 6-dimensional EEG signals, of 50 participants, with varying advantage distillation and information reconciliation strategies with and without additional lossless compression block. Across all proposed systems and analyzed sources on average, the best machine learning strategy, called the hybrid strategy, increases the quantity of generated keys 2.77 times compared to the classical strategy. By introducing the Huffman lossless coder before the PA block, the loss of potential source randomness was reduced from 68.48% to a negligible 0.75%, while the leakage rate per one bit remains in the order of magnitude 10⁻⁴.