Unlike black-box cryptography, an adversary in a white-box security model has full access to the implementation of the cryptographic algorithm. Thus, white-box implementation of cryptographic algorithms is more practical. Nevertheless, in recent years, there is no white-box implementation for public key cryptography. In this paper, we propose the first white-box implementation of the identity-based signature scheme in the IEEE P1363 standard. Our main idea is to hide the private key to multiple lookup tables, so that the private key cannot be leaked during the algorithm executed in the untrusted environment. We prove its security in both black-box and white-box models. We also evaluate the performance of our white-box implementations, in order to demonstrate utility for real-world applications.

As one type of machine-learning model, a “decision-tree ensemble model” (DTEM) is represented by a set of decision trees. A DTEM is mainly known to be valid for structured data; however, like other machine-learning models, it is difficult to train so that it returns the correct output value (called “prediction value”) for any input value (called “attribute value”). Accordingly, when a DTEM is used in regard to a system that requires reliability, it is important to comprehensively detect attribute values that lead to malfunctions of a system (failures) during development and take appropriate countermeasures. One conceivable solution is to install an input filter that controls the input to the DTEM and to use separate software to process attribute values that may lead to failures. To develop the input filter, it is necessary to specify the filtering condition for the attribute value that leads to the malfunction of the system. In consideration of that necessity, we propose a method for formally verifying a DTEM and, according to the result of the verification, if an attribute value leading to a failure is found, extracting the range in which such an attribute value exists. The proposed method can comprehensively extract the range in which the attribute value leading to the failure exists; therefore, by creating an input filter based on that range, it is possible to prevent the failure. To demonstrate the feasibility of the proposed method, we performed a case study using a dataset of house prices. Through the case study, we also evaluated its scalability and it is shown that the number and depth of decision trees are important factors that determines the applicability of the proposed method.

Cloud computing is a unlimited computing resource and storing resource, which provides a lot of convenient services, for example, Internet and education, intelligent transportation system. With the rapid development of cloud computing, more and more people pay attention to reducing the cost of data management. Data sharing is a effective model to decrease the cost of individuals or companies in dealing with data. However, the existing data sharing scheme cannot reduce communication cost under ensuring the security of users. In this paper, an anonymous and traceable data sharing scheme is presented. The proposed scheme can protect the privacy of the user. In addition, the proposed scheme also can trace the user uploading irrelevant information. Security and performance analyses show that the data sharing scheme is secure and effective.

Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achieve input-output mappings with a given data set. In this paper, we propose the non-negative task matrix decomposition method, which applies non-negative matrix factorization to a trained layered neural network. This enables us to decompose the inference mechanism of a trained layered neural network into multiple principal tasks of input-output mapping, and reveal the roles of hidden units in terms of their contribution to each principal task.

Radio signals show small characteristic differences between radio transmitters resulted from their idiosyncratic hardware properties. Based on the parameters estimation of transmitter imperfections, a novel radiometric identification method is presented in this letter. The fingerprint features of the radio are extracted from the mismatches of the modulator and the nonlinearity of the power amplifier, and used to train a support vector machine classifier to identify the class label of a new data. Experiments on real data sets demonstrate the validation of this method.

The demand response is attracting attention to perform electric power load leveling. In this paper, we consider a power consumption reduction problem with an aggregator that requests electric power consumption reduction to consumers by allocating a part of its profit to them as an incentive. We formulate interactions among consumers as a game, where the incentive to each consumer is determined by his/her contribution to the total power consumption reduction, and the consumer determines his/her own reduction amount selfishly to maximize his/her payoff. The uniqueness of best responses of each consumer and an equilibrium condition of the game are also derived. By using numerical simulations, we show relationship among incentive allocation rate, realized total reduction amount through the game, and the aggregator's payoff for the cases with the for-profit and the nonprofit aggregator.

S-shaped nonlinearity is found in the electrical resistance-length relationship in an electroactive supercoiled polymer artificial muscle. The modulation of the electrical resistance is mainly caused by the change in the contact condition of coils in the artificial muscle upon deformation. A mathematical model based on logistic function fairly reproduces the experimental data of electrical resistance-length relationship.

This paper proposes a release-aware bug triaging method that aims to increase the number of bugs that developers can fix by the next release date during open-source software development. A variety of methods have been proposed for recommending appropriate developers for particular bug-fixing tasks, but since these approaches only consider the developers' ability to fix the bug, they tend to assign many of the bugs to a small number of the project's developers. Since projects generally have a release schedule, even excellent developers cannot fix all the bugs that are assigned to them by the existing methods. The proposed method places an upper limit on the number of tasks which are assigned to each developer during a given period, in addition to considering the ability of developers. Our method regards the bug assignment problem as a multiple knapsack problem, finding the best combination of bugs and developers. The best combination is one that maximizes the efficiency of the project, while meeting the constraint where it can only assign as many bugs as the developers can fix during a given period. We conduct the case study, applying our method to bug reports from Mozilla Firefox, Eclipse Platform and GNU compiler collection (GCC). We find that our method has the following properties: (1) it can prevent the bug-fixing load from being concentrated on a small number of developers; (2) compared with the existing methods, the proposed method can assign a more appropriate amount of bugs that each developer can fix by the next release date; (3) it can reduce the time taken to fix bugs by 35%-41%, compared with manual bug triaging;

In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.

In the theory of orthogonal arrays, an orthogonal array (OA) is called schematic if its rows form an association scheme with respect to Hamming distances. In this paper, we study the Hamming distances of any two rows in an OA, construct some schematic OAs of strength two and give the positive solution to the open problem for classifying all schematic OAs. Some examples are given to illustrate our methods.

In this paper, we consider the design problem of an unknown-input observer for distributed network systems under the existence of communication delays. In the proposed method, each node estimates all states and calculates inputs from its own estimate. It is assumed that the controller of each node is given by an observer-based controller. When calculating each node, the input values of the other nodes cannot be utilized. Therefore, each node calculates alternative inputs instead of the unknown inputs of the other nodes. The alternative inputs are generated by own estimate based on the feedback controller of the other nodes given by the assumption. Each node utilizes these values instead of the unknown inputs when calculating the estimation and delay compensation. The stability of the estimation error of the proposed observer is proven by a Lyapunov-Krasovskii functional. The stability condition is given by a linear matrix inequality (LMI). Finally, the result of a numerical simulation is shown to verify the effectiveness of the proposed method.

This work develops a system called CLAP that detects and classifies “potentially unwanted applications” (PUAs) such as adware or remote monitoring tools. Our approach leverages DNS queries made by apps. Using a large sample of Android apps from third-party marketplaces, we first reveal that DNS queries can provide useful information for detection and classification of PUAs. We then show that existing DNS blacklists are limited when performing these tasks. Finally, we demonstrate that the CLAP system performs with high accuracy.

A method to reconstruct the surface shape of a scatterer from the relative intensity of the scattered field is proposed. Reconstruction of the scatterer shape has been studied as an inverse problem. An approach that employs boundary-integral equations can determine the scatterer shape with low computation resources and high accuracy. In this method, the reconstruction process is performed so that the error between the measured far field of the sample and the computed far field of the estimated scatterer shape is minimized. The amplitude of the incident wave at the sample is required to compute the scattered field of the estimated shape. However, measurement of the incident wave at the sample (measurement without the sample) is inconvenient, particularly when the output power of the wave source is temporally unstable. In this study, we improve the reconstruction method with boundary-integral equations for practical use and expandability to various types of samples. First, we propose new boundary-integral equations that can reconstruct the sample shape from the relative intensity at a finite distance. The relative intensity is independent from the amplitude of the incident wave, and the reconstruction process can be performed without measuring the incident field. Second, the boundary integral equation for reconstruction is discretized with boundary elements. The boundary elements can flexibly discretize various shapes of samples, and this approach can be applied to various inverse scattering problems. In this paper, we present a few reconstruction processes in numerical simulations. Then, we discuss the reason for slow-convergence conditions and introduce a weighting coefficient to accelerate the convergence. The weighting coefficient depends on the distance between the sample and the observation points. Finally, we derive a formula to obtain an optimum weighting coefficient so that we can reconstruct the surface shape of a scatterer at various distances of the observation points.

This paper analyzes the effect of error propagation of decision feedback equalizer (DFE) for PAM4 based 400Gb/s Ethernet. First, an analytic model for the error propagation is proposed to estimate the probability of different burst error length due to error propagation for PAM4 link system with multi-tap TX FFE (Feed Forward Equalizer) + RX DFE architecture. After calculating the symbol error rate (SER) and bit error rate (BER) based on the probability model, the theoretical analysis about the impact of different equalizer configurations on BER is compared with the simulation results, and then BER performance with FEC (Forward Error Correction) is analyzed to evaluate the effect of DFE error propagation on PAM4 link. Finally, two FEC interleaving schemes, symbol and bit interleaving, are employed in order to reduce BER further and then the theoretical analysis and the simulation result of their performance improvement are also evaluated. Simulation results show that at most 0.52dB interleaving gain can be achieved compared with non-interleaving scheme just at a little cost in storing memory and latency. And between the two interleaving methods, symbol interleaving performs better compared with the other one from the view of tradeoff between the interleaving gain and the cost and can be applied for 400Gb/s Ethernet.

Tea sprouts segmentation via machine vision is the core technology of tea automatic picking. A novel method for Tea Sprouts Segmentation based on improved deep convolutional encoder-decoder Network (TS-SegNet) is proposed in this paper. In order to increase the segmentation accuracy and stability, the improvement is carried out by a contrastive-center loss function and skip connections. Therefore, the intra-class compactness and inter-class separability are comprehensively utilized, and the TS-SegNet can obtain more discriminative tea sprouts features. The experimental results indicate that the proposed method leads to good segmentation results, and the segmented tea sprouts are almost coincident with the ground truth.

Time-sequence data is high dimensional and contains a lot of information, which can be utilized in various fields, such as insurance, finance, and advertising. Personal data including time-sequence data is converted to anonymized datasets, which need to strike a balance between both privacy and utility. In this paper, we consider low-rank matrix factorization as one of anonymization methods and evaluate its efficiency. We convert time-sequence datasets to matrices and evaluate both privacy and utility. The record IDs in time-sequence data are changed at regular intervals to reduce re-identification risk. However, since individuals tend to behave in a similar fashion over periods of time, there remains a risk of record linkage even if record IDs are different. Hence, we evaluate the re-identification and linkage risks as privacy risks of time-sequence data. Our experimental results show that matrix factorization is a viable anonymization method and it can achieve better utility than existing anonymization methods.

This paper proposes a simple source data exchange method for channel switching in space-time block code. If one transmits source data on another antenna, then the receiver should change combining method in order to adapt it. No one except knowing the channel switching sequence can decode the received data correctly. In case of exchanging data for channel switching, four orthogonal frequency division multiplexing symbols are exchanged according to a format of space-time block code. In this paper, I proposes two simple sign exchanges without exchanging four orthogonal-frequency division multiplexing symbols which occurs a different combining and channel switching method in the receiver.

In this paper, we extend the notion of bijective connection graphs to introduce directed bijective connection graphs. We propose algorithms that solve the node-to-set node-disjoint paths problem and the node-to-node node-disjoint paths problem in a directed bijective connection graph. The time complexities of the algorithms are both O(n4), and the maximum path lengths are both 2n-1.

This paper studies the joint optimization of precoding, transmit power and data rate allocation for energy-efficient full-duplex (FD) cloud radio access networks (C-RANs). A new nonconvex problem is formulated, where the ratio of total sum rate to total power consumption is maximized, subject to the maximum transmit powers of remote radio heads and uplink users. An iterative algorithm based on successive convex programming is proposed with guaranteed convergence to the Karush-Kuhn-Tucker solutions of the formulated problem. Numerical examples confirm the effectiveness of the proposed algorithm and show that the FD C-RANs can achieve a large gain over half-duplex C-RANs in terms of energy efficiency at low self-interference power levels.

Cloud computing enables computational resource-limited devices to economically outsource much computations to the cloud. Modular exponentiation is one of the most expensive operations in public key cryptographic protocols, and such operation may be a heavy burden for the resource-constraint devices. Previous works for secure outsourcing modular exponentiation which use one or two untrusted cloud server model or have a relatively large computational overhead, or do not support the 100% possibility for the checkability. In this letter, we propose a new efficient and verifiable algorithm for securely outsourcing modular exponentiation in the two untrusted cloud server model. The algorithm improves efficiency by generating random pairs based on EBPV generators, and the algorithm has 100% probability for the checkability while preserving the data privacy.