Private Information Retrieval (PIR) is a mechanism for efficiently downloading messages while keeping the index secret. The information-theoretic upper bound on efficiency has been proved in previous studies; PIR properties and the proofs of capacity were notated in terms of entropy and probability. However, in order to construct a linear PIR, it is necessary to clarify the properties of the query matrix. In this study, we prove the necessary and sufficient conditions for PIR properties, and represent them in matrix form. We also show a PIR construction method that satisfies the conditions.
A visual cryptography scheme (VCS) is one of the secret sharing schemes for digital images. In the VCS, $n$ images called shares are generated from a secret image and are distributed to $n$ respective participants. In this paper, we first give a new construction of basis matrices of the VCS realizing the $(2, n)$-threshold access structure. We use a combinatorial design known as an affine resolvable BIBD. In the construction, the relative difference is almost equal to the optimal value, while the pixel expansion is almost half compared with an existing construction attaining the optimal value if we use a $(4l, 2l, 2l-1)$ affine resolvable BIBD. We also give a construction of basis matrices using a $(4l, 2l, 2l-1)$ affine resolvable BIBD for a certain access structure. In this access structure, participants are partitioned into disjoint groups of size two and the two participants belonging to any group can reproduce a secret image. In addition, any three participants from different groups can obtain no information on a secret image.
This study aims to identify rectangular regions where the features follow an independent and identically distributed probability. To achieve this, we employ a method that divides large regions into smaller ones. We model the image using a quadtree structure and assume prior distributions for the image, labels, and quadtree. Using Bayesian decision theory, we derive the optimal estimation solution. Few studies have considered prior distributions on images for optimal estimation. We reduced computational complexity by selecting an appropriate prior distribution for the quadtree. Additionally, we estimated the parameters of each probability distribution using the training data.
We consider the tracking problem of an object that can randomly appear on a line of a fixed length. We want to determine the position of the object and rely on sensors that can detect objects with a predefined range and report that to a remote observer. Sensors are equipped with a transmitter that can transmit at a limited data rate. Once a sensor is triggered, it transmits its own ID to inform. A straightforward protocol is to label each sensor with different ID. However, this would require a large number of unique IDs and many bits to represent. As a result, higher data rate is required. We propose a newly defined protocol using multiset color coding with optimal design or efficiency in reusing a much smaller number of IDs for the whole system. We only require the minimal number of bits for labeling each sensor. We derive the factor of reduction and show its significance. We present some optimal constructions for the required multiset color coding sequence. Besides, we derive the general upper and lower bounds for the maximum length (size) of the system. Numerical examples have also demonstrated the effectiveness and improvement by the proposed new method.
This paper examines a security enhancement technique from a passively secure hierarchical identity-based identification (HIBI) protocol to a concurrently secure one. Two types of security enhancement techniques for the identification protocols have been proposed: one based on the OR-proof technique and the other using a chameleon hash function. The former has been examined in detail, while the latter has not been formulated in the HIBI protocol, and its close evaluation of applicability, especially in identity-selecting settings, and reduction efficiency has not been made public. We describe a transformation using a chameleon hash function and compare it with the others based on the OR-proof technique in applicability and reduction efficiency.
With the spread of cloud computing, information leakage from the cloud is recognized as one of the significant issues. Searchable symmetric encryption has been expected to be a core technology that fundamentally solves this problem. On the other hand, the technology has the security advantage of protecting data in the cloud, instead of the users being responsible for strictly controlling their secret keys. Computer environments and IT literacy vary from user to user, therefore it is important to note that users may be exposed to the risk of attackers gaining unauthorized access to their secret keys. This background underlines the importance of user's key management. In this paper, we propose a method that secures and simplifies this process by encrypting and storing the secret key of SSE using a fuzzy extractor. This method not only enhances the security of SSE but also offers significant benefits for end users, such as the ability to operate in multiple computer environments, thereby providing a more flexible and user-friendly experience. We implemented the proposed method on a laptop computer running Windows 10 and a smartphone running Android OS, which are the users' typical execution environments. As a result, we confirmed that the proposal performs practical application.
This article presents RF integrated receiver consists of high-gain broadband low noise amplifier (LNA) with balun, Gm-C band-pass filter, and ultra-wideband (UWB) antenna with band-notch design. The proposed wireless integrated CMOS design and analysis are chosen as the receiver of RF circuits to enhance conversion gain low noise amplifier while maintaining high-flatness at broadband. The integrated circuit is fabricated in tsmc 0.18-mu m process. From 3 to 6 GHz, the measured results exhibit high-gain of 16.2 to 17.4dB, noise figure of 4.0dB to 4.4dB with Input-referred third-order intercept point (IIP3) of -20.2 dBm while consuming 14 mA from a 1.8V supply. The measured results exhibit high-gain of 11.7 dB to 13.7 dB, noise figure of 4.6 dB to 4.9 dB with Input-referred third-order intercept point (IIP3) of -20.9 dBm while consuming 6 mA from a 1.0V supply, respectively.
Two sequences whose aperiodic autocorrelation functions sum to form a perfect impulse constitute a Golay complementary sequence pair, each being a Golay complementary sequence. These sequences have many applications in wireless communication, ultrasound, and radar systems. When used in the frequency domain of orthogonal frequency division multiplexing signals, the time domain waveform's peak-to-mean envelope power ratio (PMEPR) is upper bounded by 2. We show that the paraunitary construction proposed by Budisin and Spasojevic can create 8-QAM Golay complementary sequences for all lengths where BPSK and QPSK Golay complementary sequences were previously unknown. Through recursive and paraunitary constructions, Golay complementary sequences can be generated for all sequence lengths. Furthermore, this result enables the construction of 8-QAM unitary matrices of all even orders, expanding the potential applications of Golay complementary sequences.
This article proposes a novel approach to object detection in low-illumination conditions, an area where existing methods often fall short. By recognizing the crucial role of image quality in subsequent detection accuracy, we introduce preprocessing steps involving image enhancement and restoration networks. Our detection network then focuses on enhancing both channel and spatial feature information, integrating adaptive attention mechanisms and a Transformer architecture for contextual understanding. A multi-scale fusion strategy is employed to merge these features effectively. Additionally, we implement a partial cross-stage network to facilitate CNN learning while minimizing model size. Experimental results demonstrate the superiority of our approach over previous methods, showcasing significant accuracy improvements, particularly in challenging scenarios. This work underscores the importance of addressing image quality concerns in object detection tasks, especially in low-illumination environments, and offers a promising solution with practical implications across various domains, including autonomous driving and surveillance.
This paper develops bounds for learning lossless source coding under the PAC (probably approximately correct) framework. The paper considers iid sources with online learning: first the coder learns the data structure from training sequences. When presented with a test sequence for compression, it continues to learn from/adapt to the test sequence. The results show, not unsurprisingly, that there is little gain from online learning when the training sequence length is much longer than the test sequence length. But if the test sequence length is longer than the training sequence, there is a significant gain. Coders for online learning has a somewhat surprising structure: the training sequence is used to estimate a confidence interval for the distribution, and the coding distribution is found through a prior distribution over this interval.
Time-based One-Time Password (TOTP) is commonly used in many digital services to meet the increasing demands of security in user authentication. The security of TOTP owes much to the management of TOTP seeds from which onetime passwords are computed, but there is a certain risk of the leakage of TOTP seeds in practice. This study aims to bring a mechanism that virtually realizes the expiration of TOTP seeds. Even if a seed is left unattended or exposed to somebody at a certain point in time, the seed expires as time passes. The mechanism is developed by using the logistic map, together with careful control of numeric values that is necessary to avoid issues caused by finite-precision calculations. The paper sketches the proposed scheme and introduces the results of numerical investigations for discussing choices of good parameters.
We proposed an algorithm for the server in ARQ scheme for broadcast channel. In this study, we consider a model in which the feedback delay is a random variable. Specifically, the feedback delay is different for each packet and receiver. For this model, the algorithm proposed in conventional studies cannot be directly applied. Therefore, we propose an algorithm using windows, which are set for the sequence of packets. It is also found that there is a trade-off between throughput and acceptance time in proposed algorithm.
Given a default distribution P and a set of test data x(M) = {x(1), x(2), ... , x(M)} this paper seeks to answer the question if it was likely that x(M) was generated by P. For discrete distributions, the definitive answer is in principle given by Kolmogorov-Martin-Lof randomness. In this paper we seek to generalize this to continuous distributions. We consider a set of statistics T-1(x(M)), T-2(x(M)), ... . To each statistic we associate its maximum entropy distribution and with this a universal source coder. The maximum entropy distributions are subsequently combined to give a total codelength, which is compared with - log P(x(M)). We show that this approach satisfied a number of theoretical properties. For real world data P usually is unknown. We transform data into a standard distribution in the latent space using a bidirectional generate network and use maximum entropy coding there. We compare the resulting method to other methods that also used generative neural networks to detect anomalies. In most cases, our results show better performance.
In this work, we propose a system for joint security and error correction at the physical layer. The proposed approach combines the ideas from the area of wiretap channel coding together with a variant of the McEliece public key cryptosystem. The system makes use of randomly chosen nonbinary low-density parity-check codes with quasi-cyclic base matrices. Such a choice allows for efficient hiding of the structure of the generator matrix of the code, thus allowing for protection of the data against the eavedropper's attacks. At the same time, it also supports an efficient error correction. The analysis of the complexity of the attacks and the simulation results demonstrate the advantages of the proposed system.
The stream version of asymmetric binary systems (ABS) developed by Duda is an entropy coder for information sources with a finite alphabet. It has the state parameter l of a nonnegative integer and the probability parameter p with 0 < p < 1. The algorithm of stream encoding yields the edge shift X-G associated with the stream version of ABS while the algorithm produces the edge shift X-H associated with output blocks from the stream version of ABS. Previously, we have shown that X-G and X-H have the same topological entropy. In this research, we find that X-G and X-H are flow equivalent. We consider the case where p = 1/beta with the golden mean beta = (1/root 5)/2. For several l's, we compute the Parry-Sullivan quantity D(A(G)) and the Bowen-Franks group BF(A(G)) since they form a complete set of flow equivalence invariants for irreducible shift of finite type. The results imply that for a fixed p, D(A(G)) and BF(A(G)) depend on l.
For efficient use of energy in quantum communication, it is best to use discrete signals that approach the classical capacity achieved by a continuous input of coherent states. Analytical treatment of the classical communication channel requires discretization, and among such discrete signals, using signals as close as possible to the quantum channel capacity is ideal. The class of binary phase shift keying (BPSK) coherent-state signals has been proven to be optimal in the case of binary discretization; however, for multiary discretization with three or more signals, the optimal signals are unknown. In this study, we consider the case of ternary discretization. We numerically show that 3PSK is the optimal signal constellation in two dimensions.
This paper introduces a deep unfolding-assisted proximal decoding for low-density parity-check (LDPC) codes. Proximal decoding method is a decoding algorithm based on the proximal gradient method. Our proposal, deep unfolding-assisted proximal (DU-Proximal) decoding, is obtained by applying deep unfolding to the proximal decoding. We especially focus on the decoding performance in multiple-input multiple-output (MIMO) channels. In numerical experiments, we compare the error correcting capability of the proposed algorithm with the minimum mean squared error (MMSE) signal detection method, the tanh signal detection method, and the MMSE jointly with belief propagation (BP) algorithm for LDPC decoding. The experimental results demonstrate that parameter optimization via DU yields significant performance gains, especially at high signal-to-noise ratio levels.
Almost all problems in multi-terminal information theory stem from Slepian-Wolf coding problem and a coding problem for multiple access channels (MACs). Cover et al. considered a joint source-channel coding problem and integrated these two problems. One typical example of the communication model proposed by Cover et al. is a sensor network. However, in usual sensor networks, the transmission power of sensors is so small that each sensor cannot send the message to the data center directly, but the nearest relay or edge node, and the relay node resends the message to the data center through a backborn network. We model such a sensor network as MACs employing relay nodes that consist of a cascade of point-to-point channels and a MAC. We show a sufficient condition and a necessary condition for reliable communication over a MAC employing relay nodes. Moreover, we show that both conditions coincide if the sources are independent.
CAPTCHA is an authentication test that distinguishes between humans and machines. CAPTCHA is essential to prevent fraudulent account acquisition by BOTs. Conventional CAPTCHAs were based on the assumption that human abilities were superior to BOTs, but with the evolution of AI technology, this assumption no longer holds true. As CAPTCHAs have become more complex, usability has been compromised. The Munker illusion is a visual illusion in which shapes of the same color appear to be different colors to the human eye due to the synergistic effect of color assimilation and color contrast. In this paper, we propose a new CAPTCHA using the Munker illusion.
Decision tree algorithms are one of the most popular methods in machine learning. However, most decision tree algorithms do not assume a stochastic model behind data. On the other hand, a meta-tree was recently proposed as a stochastic model with a tree structure. The prediction under the assumption of the meta-tree is decided using Bayesian decision theory. Although the optimal prediction can be calculated with an assumption of a known meta-tree, an approximation is necessary to obtain a prediction under the problem setting of an unknown meta-tree because of the marginalization of all possible meta-trees. In this paper, we propose an approximation method, where a subset of meta-trees is sequentially constructed, and the prediction is made by weighting the meta-trees. For the approximation, we clarify the approaches of constructing the subset and making predictions with weighted meta-trees. We also examine the effectiveness of the approaches in an experiment using synthetic data. In addition, we conduct an experiment on benchmark data to confirm the performance of the proposal.
