Voice spoofing detection is a technique for enhancing the security of automatic speaker verification system, but the existing research still faces problems such as weak detection capability and expensive computation. To address these problems, this work presents a lightweight voice anti-spoofing method by using improved one-class learning DOC-Softmax and knowledge distillation. The main idea of DOC-Softmax is to learn a feature space where the genuine samples have a compact space and the spoofing samples are parted from the bona fide space by a certain interval. And the dispersion loss is introduced for spoofing samples to cover the whole spoofing space as much as possible. Moreover, a lightweight voice spoofing detection model is designed to speed up inference, and the knowledge distillation is employed to improve representation power of the lightweight model. Without any data augmentation and ensemble learning, a series of experiments are conducted on LA and PA scenarios of the ASVspoof 2019 dataset, and the experimental results indicate that the proposed method performs better than most existing voice anti-spoofing methods.

Most of the voice spoofing detection methods are designed for specific kinds of spoofing attacks, synthetic or replay. In practice, however, there is no prior information about these two kinds of spoofing attacks. To this end, this paper proposes a generalized voice spoofing detection method based on integral knowledge amalgamation to detect jointly synthetic attacks and replay attacks. Two amalgamation mechanisms, feature amalgamation and structure amalgamation, are designed from different perspectives, so that the model can generalize better and run fast. Specifically, the feature amalgamation transfers the high-level sematic knowledge from two teacher models to the compact model. The structure amalgamation employs the adversarial learning to ensure the global structure consistency of two teacher models and a student model. In addition, the feature matching loss is introduced to capture the distinctive features of synthetic attacks and replay attacks. We conduct extensive experiments on logical access (LA) scenario and physical access (PA) scenario of ASVspoof 2019 dataset to verify the validity of the proposed method. The experimental results show that compared with the most advanced generalized voice spoofing detection methods, the proposed method achieves a comparable or even better performance. In particular, our method gains the state-of-the-art detection capability on LA scenario. Moreover, our method achieves similar or even outstanding detectability when compared with specialized anti-spoofing methods.

In cloud-based electronic medical record (EMR) systems, attribute-based encryption (ABE) has been utilized to protect the confidentiality of EMRs and provide keyword search over the encrypted EMRs. However, existing schemes are designed for a single attribute authority, and lack sufficient user privacy protection. In this article, we introduce TABKS, a privacy-preserving traceable attribute-based keyword search scheme in multi-authority medical cloud. First, we propose an anonymous EMR access control framework with multiple authorities, which provides user anonymity against the untrusted authorities. Second, we achieve traceable attribute-based Boolean keyword search, which enables the authorized user who satisfies the policy to conduct Boolean keyword search over the encrypted EMRs. In this process, TABKS improves the efficiency of legitimate users by partially decrypting the matched results, and also achieves efficient traitor trace by revealing the user identity from the trapdoor. Finally, we prove the security of TABKS against chosen plaintext attack and chosen keyword attack, and conduct extensive experiments with two real-world datasets to show the feasibility of TABKS.

Currently, various encryption techniques have been employed to protect the documents in cloud storage. In particular, attribute-based keyword search (ABKS) is a practical encryption primitive that can realize fine-grained access control and keyword based searching over encrypted documents. However, the search time in most of the existing ABKS schemes increases linearly with the size of document collection, which hinders the wide application of ABKS in cloud computing. To this end, we propose FAKS, a fast and privacy-preserving attribute-based keyword search system for cloud document services. Specifically, FAKS builds a Bloom filter tree structure from the document collection, which avoids matching keywords by traversing the entire collection. Then we introduce an attribute-based authenticated index retrieval (ABAIR) scheme to encrypt the Bloom filters in the tree node and retrieve the documents with the encrypted Bloom filters of the query keywords. Further, we give a concrete construction of FAKS from ABAIR to execute the keyword matching operations sublinearly in a top-down manner, and prove the security of FAKS against chosen keyword attack and keyword guessing attack. Finally, we conduct extensive experiments over the Wikipedia dataset, which show better and more stable search efficiency of FAKS compared to existing schemes.

The identity authentication of most applications is based on a symbolic password. However, incidents of password leakage emerge one after another, which brings serious hidden danger to the users’ information security. For decades, various schemes have been proposed to solve the problem of information protection. However, most schemes neglect the timely detection of password leakage. The present paper introduces a password leak detection method based on zero-divisor graph sequences. Specifically, it is to construct an algorithm for generating honeywords with high smoothness. First, we introduce the concept of the zero-divisor graph and construct zero-divisor graph sequences by using the corresponding zero-divisor matrices. Second, the honeywords with high flatness are constructed by using the sequence of zero-divisor graphs. Third, the security analysis verifies the effectiveness of the scheme. Fourth, compared with other honeywords schemes, our scheme has more obvious advantages, in the aspects of honeywords generated flatness, DoS resistance, and storage resources occupied by honeywords.

Source location privacy (SLP) protection is an emerging research topic in wireless sensor networks. Because the source location represents the valuable information of the target being monitored and tracked, it is of great practical significance to achieve a high degree of privacy of the source location. Although many studies based on phantom nodes have alleviates the protection of SLP to some extent. It is urgent to solve the problems, such as complicate the ac path between nodes, improve the centralized distribution of phantom nodes near the source nodes and reduce the network communication overhead. In this paper, protection scheme based on sector phantom routing (PSSPR) routing is proposed as a visible approach to address SLP issues. We use the coordinates of the center node V to divide sector domain, which act an important role in generating a new phantom node. The phantom nodes perform specified routing policies to ensure that they can choose various locations. In addition, the directed random route can ensure that data packets avoid the visible range when they move to the sink node hop by hop. Thus, the source location is protected. Theoretical analysis and simulation experiments show that this protocol achieves higher security of source node location with less communication overhead.

Selfish mining attacks get a high prize due to the additional rewards unproportionate to their mining power (mining pools have particular advantages). Generally, this category of attacks stresses decreasing the threshold to maximize the rewards toward the view of attackers. Semi-selfish mining falls into the family of selfish mining attacks, where the threshold value is approximately 15%. However, it gets little attention to implement these attacks in practical. In this paper, we focus on the validity of semi-selfish mining attacks considering the probability of being detected. More specifically, we discuss mining strategies through backward deduction. That is to say that the attacking states derived from the observable states, which with normal forking rate, just as without semi-selfish mining attacks, toward the view of the honest miners. Rewards distribution is further investigated concerning these strategies. The simulation results indicate that it does not necessarily bring rewards advantage over large pools. Instead, the small pools have an advantage over the additional rewards. However, the probability for small pools to successfully implement these strategies is pretty low. That is, it is impossible for the pools, although profitable for them, to sponsor semi-selfish mining attacks without being detected.

With the rapid development of bioinformatics and the availability of genetic sequencing technologies, genomic data has been used to facilitate personalized medicine. Cloud computing, features as low cost, rich storage and rapid processing can precisely respond to the challenges brought by the emergence of massive genomic data. Considering the security of cloud platform and the privacy of genomic data, we first introduce P2GT which utilizes key-policy attribute-based encryption to realize genomic data access control with unbounded attributes, and employs equality test algorithm to achieve personalized medicine test by matching digitized single nucleotide polymorphisms (SNPs) directly on the users' ciphertext without encrypting multiple times. We then propose an enhanced scheme P2GT+, which adopts identity-based encryption with equality test supporting flexible joint authorization to realize privacy-preserving paternity test, genetic compatibility test and disease susceptibility test over the encrypted SNPs with P2GT. We prove the security of proposed schemes and conduct extensive experiments with the 1,000 Genomes dataset. The results show that P2GT and P2GT+ are practical and scalable enough to meet the privacy-preserving and authorized genetic testing requirements in cloud computing.

With the popularity of social networks, a great variety of new social applications have been generated for impromptu group formation and communications. Among those applications, the subgraph matching has become a hot research area in social networks. Due to the huge cost of managing and computing graph data, it may have to outsource the computations to the cloud server. However, the most critical problem is that the cloud server leaks the graph information during the processing of the graph data, and the external attackers modify the graph information during the transmission on the public channel. Thus, confidentiality and authentication have been critical attributes in the subgraph matching query service. In this article, we present an efficient and privacy-preserving subgraph matching scheme with authentication in social networks. Using the proposed scheme, the cloud can accomplish the subgraph matching query process without obtaining any sensitive information about the users. Additionally, we achieve data integrity verification and user authentication. Each receiver can verify if the received messages come from the legal sender and have not been tampered. The detailed security and efficiency analysis show that the proposed scheme not only satisfies security requirements but also achieves high-efficiency in local users, and it is suitable for many practical applications.

Locally repairable codes (LRCs) are important for distributed storage systems due to their efficient repairing ability of the failed storage nodes. A$q$-ary optimal$(n,k,r)$-LRC is an$[n,k,d]$linear code over$\mathbb {F}_{q}$such that every code symbol has locality$r$, and the minimum distance attains the well-known Singleton-like bound. In this paper, we study the maximal code length, code constructions and weight distributions of$q$-ary optimal LRCs with locality 2 and distance 5, which are of both practical and theoretical interest. Firstly, it is proved that when the code dimension is even or odd, corresponding maximal code lengths of such$q$-ary optimal LRCs are$3 \cdot \lfloor \frac {q+1}{3} \rfloor $and$3 \cdot \left \lfloor{ \frac {q-1}{3} }\right \rfloor +5$, respectively. Up to the equivalence of linear codes, we propose constructions of all the possible$q$-ary optimal LRCs with locality 2, distance 5 and maximal code length. Then, by characterizing the weight type hierarchy of codewords, we show that the weight distribution of any$q$-ary optimal LRC with locality 2, distance 5 and even code dimension can be uniquely determined and explicit expression of the weight distribution is given. Moreover, it is shown that all$q$-ary optimal LRCs with locality 2, distance 5 and even code dimension are maximally recoverable.

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this article, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption, linear encryption and RSA encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.

Distributed cooperative control strategies of DC microgrids (DCMG) reduce the detriment of communication delays, packet loss and link failure compared with centralized control. However, they are vulnerary to cyber-attacks. The operating objectives can be deviated by false data. Firstly, the adverse effects of false data are explained and modeled. A data-driven strategy based on linear regression is then proposed to remove the false data by offline learning and online judging of the transient process in DCMG without affecting the dynamic response. It successfully solves the problem of parameter selection of resilient control. Finally, the detection strategy is verified by detailed time-domain simulation.

A$q$-ary ($n, k, r$) locally repairable code (LRC) is an [$n, k, d$] linear code where every code symbol can be repaired by accessing at most$r$other code symbols. Its minimum distance satisfies the well-known Singleton-like bound. In this paper, we determine all the possible parameters of quaternary LRCs attaining this Singleton-like bound by employing a parity-check matrix approach. Explicit optimal code constructions are given for all the possible parameters.

The contradiction between the growing demand for healthcare and the lack of professional medical staff urgently needs to be resolved. Healthcare wireless sensor technology is an important option to resolve this contradiction. Patients use wearable devices to collect various health indicators and send them to doctors for diagnosis in a timely manner. This breaks the time and space constraints in traditional medical methods. Patient's data is transmitted over a public network. Therefore, protecting data security and patient privacy has become an urgent issue for healthcare wireless sensor networks (HWSNs). In this article, we first presented a system model for certificateless designated verifier anonymous aggregate signature (CL-DVAAS) scheme for HWSNs, and investigated the security attributes. We then gave a concrete construction and showed the security proofs. We finally compared the new scheme with several other schemes, and the results show that the new scheme is more computationally efficient.

Device-to-Device (D2D) communication has now become one of the most promising technologies in wireless communications of the Internet of Things (IoT). In view of the requirements on data security, response speed, signal decryption quality and storage cost of mobile devices in D2D networks, we propose chaotic deep network (CDN) to achieve secure transmission which is swifter, higher quality and lower cost. The proposed scheme consists of a prepositive nonrepetitive training procedure, a well-designed parallel encryption process, and a set of pretrained chaotic deep neural decryption networks. Benefiting from the utilization of deep learning methods, CDN achieves the swift and accurate decryption at a much lower sampling rate, which brings huge dimension reduction of both measurement matrices and ciphertext signals. Also, chaotic initial values and parameters are applied to matrix generation and network training, leading to great time reduction, storage saving and security improvement. In addition, CDN incorporates the frameworks of semitensor product (STP) and block-based image processing (BIP), which not only breaks through the dimension matching limitation of matrix multiplication by using STP but also maintains the parallelizable block-cipher mode of BIP. Proved by experiments, CDN at the sampling ratio of 25% achieves almost the same or even better peak signal to noise ratio compared to other 8 most frequently used methods at that of 50% for the same test images, and obtains better visual effects. When using a large-sized image of$2^{10}\times 2^{10}$pixels for the experiments, CDN is dozens and even hundreds of times faster than those methods, while reduces the size of measurement matrix from a 500-kB level to a 3-kB level, and the size of compressed data to be transmitted can be reduced by more than 50% as well. Besides, the total key space is approximately$10^{93}$. The adjacent pixel correlation is less than 0.01.

Due to wealthy structure and semantic information expressed by a graph, the graph is frequently employed in numerous social applications to show social relationships. Among those important applications, the private graph intersection operation plays an important part in social networks. Because of the high cost of managing graph data and the computational difficulty of graph intersection operation, delegating the computations to the cloud server (CS) is an attractive alternative. However, when the CS is untrusted or compromised by some adversaries, the results that the CS returns can not be guaranteed to be correct. In such cases, it may have serious consequences for the application functionality. In this article, we present an efficient and privacy-preserving verifiable graph intersection scheme with cryptographic accumulators in social networks. Using the proposed scheme, we construct the framework to provide secure verifiable graph intersection operation in an untrusted cloud, and the requester can verify the correctness of the graph intersection result that the CS returns. Additionally, the data owners' graph data privacy and user authentication are well protected. The detailed correctness proof and performance analysis show that the proposed scheme is secure and feasible. Thus, our scheme is appropriate for many practical applications.

As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.

With the rapid development of cloud services, huge volume of data is shared via cloud computing. Although cryptographic techniques have been utilized to provide data confidentiality in cloud computing, current mechanisms cannot enforce privacy concerns over ciphertext associated with multiple owners, which makes co-owners unable to appropriately control whether data disseminators can actually disseminate their data. In this paper, we propose a secure data group sharing and conditional dissemination scheme with multi-owner in cloud computing, in which data owner can share private data with a group of users via the cloud in a secure way, and data disseminator can disseminate the data to a new group of users if the attributes satisfy the access policies in the ciphertext. We further present a multiparty access control mechanism over the disseminated ciphertext, in which the data co-owners can append new access policies to the ciphertext due to their privacy preferences. Moreover, three policy aggregation strategies, including full permit, owner priority and majority permit, are provided to solve the privacy conflicts problem caused by different access policies. The security analysis and experimental results show our scheme is practical and efficient for secure data sharing with multi-owner in cloud computing.

Compressed sensing is an emerging theory of signal processing and it has wide applications in many frontier fields. The construction of the measurement matrices is still a central problem in compressed sensing. In this paper, two types of deterministic constructions of binary measurement matrices are presented via unitary geometry. Then, the lower bounds of the spark of unitary geometry measurement matrices are theoretically analyzed, and an asymptotic comparison between unitary geometry measurement matrices and projective geometry measurement matrices is given via the worst-case recovery capability. After that, a clipping-embedding operation is proposed for binary matrices to generate measurement matrices with more sizes, which can strongly extend the applicability of the deterministic binary matrices in practice. Finally, simulation results demonstrate that the performance of our measurement matrices is comparable to, sometimes even better than, that of the corresponding Gaussian random matrices under OMP and BP.

Benefiting from cloud computing and mobile devices, a huge number of media contents, such as videos are shared in mobile networks. Although scalable video coding can be utilized to provide flexible adaptation, the cloud poses a serious threat to media privacy. In this paper, we propose a privacy-preserving multi-dimensional media sharing scheme named SMACD in mobile cloud computing. First, each media layer is encrypted with an access policy based on attribute-based encryption, which guarantees media confidentiality as well as fine-grained access control. Then, we present a multi-level access policy construction with secret sharing scheme. It ensures that the mobile consumers who obtain a media layer at a higher access level must satisfy the access trees of its child layers at the lower access level, which is compatible with the characteristics of multi-dimensional media and also reduces the complexity of access policies. Moreover, we introduce decentralized key servers to achieve both intra-server and inter-server deduplication by associating different access policies into the same encrypted media. Finally, we conduct experimental evaluation on mobile device and cloud platform with real-world datasets. The results indicate that SMACD protects media privacy against cloud media center and unauthorized parties, while incurring less computational and storage cost.

Cloud computing has become increasingly popular among users and businesses around the world. Although cryptographic techniques can provide data protection for users in public cloud, several issues also remain problematic, such as secure data group dissemination and fine-grained access control of time-sensitive data. In this paper, we propose an identity-based data group sharing and dissemination scheme in public cloud, in which data owner could broadcast encrypted data to a group of receivers at one time by specifying these receivers' identities in a convenient and secure way. In order to achieve secure and flexible data group dissemination, we adopt attribute-based and timed-release conditional proxy re-encryption to guarantee that only data disseminators whose attributes satisfy the access policy of encrypted data can disseminate it to other groups after the releasing time by delegating a re-encryption key to cloud server. The re-encryption conditions are associated with attributes and releasing time, which allows data owner to enforce fine-grained and timed-release access control over disseminated ciphertexts. The theoretical analysis and experimental results show our proposed scheme makes a tradeoff between computational overhead and expressive dissemination conditions.

The weight distribution of a q-ary [n, k, d] linear code is an important research subject in coding theory. In a linear code, a code symbol is said to have locality r if it can be recovered by accessing at most r other code symbols. A q-ary locally repairable code (LRC) is an [n, k, d] linear code over Fq such that every code symbol has locality r, and is said to be optimal if the minimum distance attains the well-known Singleton-like bound. In this paper, we focus on the weight distributions of q-ary optimal LRCs with locality 2, minimum distance 5 and even dimension k. By analyzing the parity-check matrices involving locality, it is shown that the weight distributions of all q-ary optimal LRCs with locality 2, distance 5, even dimension k and code length n can be uniquely determined and explicit expressions of the weight distributions are given.

With the rapid development of the Internet of Things technology and the gradual upgrade of communication methods, a new type of telemedicine system encounters a golden opportunity for development. However, lots of portable sensors for medical health have generated a large amount of sensitive data while bringing convenience to people, which makes the original transmission scheme difficult to meet current transmission requirements. Aiming at realizing high efficiency of image transmission and high security of sensitive image data in the telemedicine system, this article proposes a secure and traceable image transmission scheme. The sensitive image is first compressed and encrypted by the optimized semitensor product compressed sensing algorithm. By the perceptual hash algorithm, the image hash value, also called image fingerprint, is generated from the image content to verify the authenticity of the image. All the image fingerprints are merged to form an image fingerprint chain, which provides the traceability of the image authentication. The digital watermarking technology is used to hide the encrypted sensitive image into a meaningful carrier image, which increases the transmission content without enlarging the transmission load. The experimental results show that the proposed image transmission scheme can effectively improve the transmission efficiency, the reconstruction effect, the security, and the authenticity of the image transmission in the telemedicine systems.

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.

A locally repairable code (LRC) is a linear code such that every code symbol can be recovered by accessing a small number of other code symbols. In this paper, we study bounds and constructions of LRCs from the viewpoint of parity-check matrices. Firstly, a simple and unified framework based on parity-check matrix to analyze the bounds of LRCs is proposed, and several new explicit bounds on the minimum distance of LRCs in terms of the field size are presented. In particular, we give an alternate proof of the Singleton-like bound for LRCs first proved by Gopalan et al. Some structural properties on optimal LRCs that achieve the Singleton-like bound are given. Then, we focus on constructions of optimal LRCs over the binary field. It is proved that there are only five classes of possible parameters with which optimal binary LRCs exist. Moreover, by employing the proposed parity-check matrix approach, we completely enumerate all these five classes of optimal binary LRCs attaining the Singleton-like bound in the sense of equivalence of linear codes.

The Internet of Things (IoT) has attracted extensive attention in the information field. Its rapid development has promoted several monitoring application domains. However, the resource constraint of sensor nodes and the security of data transmission have emerged as significant issues. In this paper, an image communication system for IoT monitoring applications is exploited to solve the above-mentioned problems simultaneously. The proposed system can satisfy the requirements of sensor nodes for low computational complexity, low-energy consumption, and low storage overhead. We also present a new compressed sensing (CS) model, as well as the corresponding parallel reconstruction algorithm, which help to reduce the image encryption/decryption time. Based on chaotic systems, we integrate the quantization and diffusion operations into the system to further enhance the transmission security. The simulations are executed to demonstrate the feasibility and the effectiveness of the proposed method. Compared with the traditional CS, our numerical results indicate that the proposed model reduces 413 ms computation time and 3.13 × 106elements stored for large-scale images. Besides, we verify the flexibility and the diversity of choosing two submatrices for different-sized images. Experimental results also show the proposed system performs well in terms of security performance. Particularly the key space reaches 2253.

In a linear code, a code symbol with (r, δ)-locality can be repaired by accessing at most r other code symbols in case of at most δ - 1 erasures. A q-ary (n, k, r, δ) locally repairable codes (LRC) in which every code symbol has (r, δ)-locality is said to be optimal if it achieves the Singleton-like bound derived by Prakash et al.. In this paper, we study the classification of optimal ternary (n, k, r, δ)-LRCs (δ > 2). Firstly, we propose an upper bound on the minimum distance of optimal q-ary LRCs in terms of the field size. Then, we completely determine all the 6 classes of possible parameters with which optimal ternary (n, k, r, δ)-LRCs exist. Moreover, explicit constructions of all these 6 classes of optimal ternary LRCs are proposed in the paper.

Abstract With the development of Lie theory, Lie groups have attained profound significance in several branches of Mathematics and Physics. In Lie theory, the matrix exponential plays a crucial role between Lie groups and Lie algebras. Meanwhile, as the finite analogue of Lie groups, finite groups of Lie type have potential applications in cryptography due to their unique mathematical structures. In this paper, we first put forward a novel idea of designing cryptosystems based on Lie theory. First of all, combing with discrete logarithm problem and group factorization problem, we proposed several new intractable assumptions based on the matrix exponential in finite groups of Lie type. Subsequently, in analog with Boyen’s scheme (Asiacrypt 2007), we designed a public-key encryption scheme based on the non-abelian factorization problem in finite groups of Lie type. Finally, our proposal was proved to be indistinguishable against adaptively chosen-ciphertext attack in the random oracle model. It is encouraging that our scheme also has the potential to resist against Shor’s quantum algorithm attack.

Wireless body area networks (WBANs) collect some physiological parameters of the human body. Each sensor uses limited energy to maximize its own life. There are three crucial problems including adaptiveness, energy, and security in WBANs. In order to solve these problems, a flexible and secure data transmission system is proposed in this paper. The proposed scheme is composed of semitensor compressive sensing (CS), hash function, Arnold scrambling, and chaotic scrambling. For the adaptiveness problem, our scheme uses semitensor CS to encrypt multiple signals with different dimensions. The chaotic sequence is applied to generate the semitensor measurement matrix. On the one hand, we only transmit a few chaotic parameters, which reduces the number of data storage and transmission. On the other hand, the size of the measurement matrix is small, and the computation overhead can be reduced. The security is considered by the proposed scheme which combines Arnold scrambling and logistic scrambling to improve the encryption effect. Numerical simulations and security analyses are given to show that our scheme performs well. The total key space is approximately 2420. The absolute value of adjacent pixel correlation is less than 0.004. Traditional CS method stores 524 288 bytes, while the proposed scheme only stores 2048 bytes. When the compression ratio is less than 0.7, the peak signal to noise ratio of our scheme is obviously higher than those of other three schemes.

The dimension matching is a tough problem in the vector and matrix computations. In the traditional mode, there is only one way to calculate the angle between the 1-D plane and the 3-D vector, it is the projection. However, there are a number of lines on the plane, and taking only the projection to represent the plane is kind of a narrow choice. Furthermore, in the matrix multiplication, the dimension restriction is strict. In order to solve these problems, this paper defines a new model called P -tensor product (PTP), which cannot only define the inner product of two vectors with unmatched dimensions but also give a new way to solve the problems in the matrix operations. Aiming at decreasing the large storage space of the random matrix in compressed sensing (CS), the PTP can reconstruct a high-dimensional matrix by using a matrix, which can be chosen as any kind of matrix. Similar with the traditional CS, we analyze some reconstruction conditions of PTP-CS such as, the spark, the coherence, and the restricted isometry property. The theorems proposed in this paper have a broad sense, and they possess a good universality for various tensor product CS methods. The experimental results demonstrate that our PTP-CS model can not only give more choices to the types of Kronecker matrix and decrease the storage space of the traditional CS but also maintain the considerable recovery performance. Besides, the proposed PTP-CS model can improve the signal transmission efficiency in the Internet of Things.

Blockchain, as one of the crypto-intensive creatures, has become a very hot topic recently. Although many surveys have recently been dedicated to the security and privacy issues of blockchains, there still lacks a systematic examination on the cryptographic primitives in blockchains. To this end, we in this paper conduct a systematic study on the cryptographic primitives in blockchains by comprehensive analysis on top-30 mainstream cryptocurrencies, in terms of the usages, functionalities, and evolutions of these primitives. We hope that it would be helpful for cryptographers who are going to devote themselves to the blockchain research, and the financial engineers/managers who want to evaluate cryptographic solutions for blockchain-based projects.

Nearest neighbor (NN) models play an important role in the intrusion detection system (IDS). However, with the advent of the era of big data, the NN model has the disadvantages of low efficiency, noise sensitivity, and high storage requirement. This paper presents a neighbor prototype selection method based on CCHPSO for intrusion detection. In the model, the prototype selection and feature weight adjustment are performed simultaneously and k-nearest neighbor (KNN) is used as the basic classifier. To deal with large-scale optimization problems, a cooperative coevolving algorithm based on hybrid standard particle swarm and binary particle swarm optimization, which employs the divide-and-conquer strategy, is proposed in this paper. Meanwhile, a fitness function based on the accuracy and data reduction rate is defined in the CCHPSO to obtain a set of appropriate prototypes and feature weights. The KDD99 and NSL datasets are used to assess the effectiveness of the method. The empirical results indicate that the data reduction rate of the proposed method is very high, ranging from 82.32% to 92.01%. Compared with all the data used, the proposed method can not only achieve comparable accuracy performance but also save a lot of storage and computing resources.

Since the turn of the century, hundreds of image watermarking algorithms based on histogram features have been reported. However, all the existing watermark embedding was based on binary embedding, and they only store two situations on two continuous bins, i.e., {0,1}. In this paper, we improve the existing embedding algorithms and propose a novel high-capacity watermarking algorithm by using two bins histogram modification. The new algorithm is based on the ternary numeral system, and watermark information is divided into three cases {0,1,2}. Specifically, we extract the histogram of the cover image first, then select the appropriate embedding range by two predefined thresholds, and last form the bin groups contained reasonable number of pixels. In the generation of watermark, we transfer the watermark information into a digital string W = w1w2... wt, wi∈ (0,1,2),i = 1,2,...,t. In the embedding algorithm, if wi= 2, we will adjust the height of two continuous bins, and let b'/a' ≥ T; if wi= 1, let a'/b' ≥ T; if wi= 0, let |a'-b'| ≤ 1, here a' and b' represent the height of the front and back bin, and T is the threshold. Experimental results show that the embedding capacity of the proposed watermarking algorithm is 60% higher than that of the existing algorithm. In addition, the new scheme can resist traditional geometric attacks.

Data collaboration is more and more popular in cloud computing. In a typical collaboration scenario, data owner outsources the data to cloud platforms, and users can access and re-upload the data. In consideration of the semi-trusted cloud platform, attribute-based encryption (ABE) has been utilized to guarantee data confidentiality and fine-grained access control. However, how to allow the collaborative data to be accessed only by authorized users in a flexible and dynamic manner is a challenging problem. In this paper, we propose DACSC, a dynamic and fine-grained access control scheme for secure data collaboration in cloud computing. First of all, we adopt ciphertext-policy ABE technique to define the original access policy of outsourced data. Second, we introduce a tree-based policy extending framework which allows users who satisfy the original access policy to customize a new access policy and add it to current access policies in a non-restrictive or restrictive way. Furthermore, we achieve integrity checking during the policy extending procedure based on ABE with equality test algorithm, which ensures that the added access policy comes from authorized user. The security analysis and experimental results indicate that DACSC is secure and efficient, and is suitable for the data collaboration scenario in cloud computing.

A code symbol in an$[n,\ k,\ d]$linear code is said to have locality$r$if it can be repaired from at most$r$other code symbols. An$(n,\ k,\ r)$locally repairable code (LRC) in which every code symbol has locality$r$is said to be optimal if its minimum distance achieves the Singleton-like bound derived by Gopalan et al. In this paper, we study the maximal code length of a q-ary optimal$(n,\ k,\ r)$-LRC. Firstly, we give an upper bound on the code length of q-ary optimal LRCs, and then derive some structural properties and the weight hierarchy of optimal LRCs with maximal code length. Finally, we give some constructions of optimal q-ary LRCs with maximal code length.

Machine learning plays an important role in constructing intrusion detection models. However, the information era is an era of data. With the continuous increase in data size and the growth of data dimensions, the ability of a single classifier is becoming limited in predicting samples. In this paper, we present an ensemble method using random subspace in which an extreme learning machine (ELM) is chosen as the base classifier. To optimize the ensemble model, an ensemble pruning method based on the bat algorithm (BA) is proposed. Meanwhile, a fitness function based on the accuracy and diversity of an ensemble is defined in the BA to obtain an improved classifier subset. Three public datasets, the KDD99, NSL and Kyoto datasets, are adopted to assess the robustness of the method. The empirical results indicate that the ensemble method based on random subspace can improve the accuracy and robustness over the use of an individual ELM. The results also show that compared with when all the sub-classifiers are used in the ensemble, the pruning framework can not only achieve comparable or better performance but also save substantial computing resources in an intrusion detection system (IDS).

Data collaboration in cloud computing is more and more popular nowadays, and proxy deployment schemes are employed to realize cross-cloud data collaboration. However, data security and privacy are the most serious issues that would raise great concerns from users when they adopt cloud systems to handle data collaboration. Different cryptographic techniques are deployed in different cloud service providers, which makes cross-cloud data collaboration to be a deeper challenge. In this paper, we propose an adaptive secure cross-cloud data collaboration scheme with identity-based cryptography (IBC) and proxy re-encryption (PRE) techniques. We first present a secure cross-cloud data collaboration framework, which protects data confidentiality with IBC technique and transfers the collaborated data in an encrypted form by deploying a proxy close to the clouds. We then provide an adaptive conditional PRE protocol with the designed full identity-based broadcast conditional PRE algorithm, which can achieve flexible and conditional data re-encryption among ciphertexts encrypted in identity-based encryption manner and ciphertexts encrypted in identity-based broadcast encryption manner. The extensive analysis and experimental evaluations demonstrate the well security and performance of our scheme, which meets the secure data collaboration requirements in cross-cloud scenarios.

In this paper, we propose a novel general non-negative matrix factorization (general-NMF)-based digital watermarking scheme for copyright protection and integrity authentication of the image content. Specifically, the proposed general-NMF algorithm is able to factorize a matrix C ∈ R+s×tinto a basis matrix A ∈ R+m×nand a coefficient matrix B ∈ R+p×qby removing the dimension-matching constraints required by the conventional NMF, where s = m, n = p, and t = q. In particular, s = m · l/n, t = l/p · q, and the variable l is the least common multiple of n and p. Furthermore, the generator factor of the random matrix and n are used as the keys of the proposed digital watermarking scheme. Experimental results show that the proposed digital watermarking scheme can effectively resist various attacks and tampering.

This paper studies the problems of “security meridian-collateral” and “security confrontation” systematically and refreshes the traditional concept of security. On the one hand, based on the model of “meridians” in Chinese traditional medicine for the first time, this paper proves the following results strictly by the probability method. There is a complete “meridian-collateral diagram” in Internet of things and any finite system, so that any “sickness” of the system can be cured effectively. On the other hand, this paper studies the network attack and defense from the perspective of the information theory. Through mathematical modeling, based on the famous Shannon's coding theorem, the research on the ability problem of attacker and defender is transformed into the research on the channels of attacker and defender. From the perspective of the channel capacity in information theory for the first time, this paper gives the reachable theoretical limitation of the attack ability of hacker and the defend ability of honker precisely.

Mobile healthcare social networks (MHSN) integrated with connected medical sensors and cloud-based health data storage provide preventive and curative health services in smart cities. The fusion of social data together with real-time health data facilitates a novel paradigm of healthcare big data analysis. However, the collaboration of healthcare and social network service providers may pose a series of security and privacy issues. In this paper, we propose a secure health and social data sharing and collaboration scheme in MHSN. To preserve the data privacy, we realize secure and fine-grained health data and social data sharing with attribute-based encryption and identity-based broadcast encryption techniques, respectively, which allows patients to share their private personal data securely. In order to achieve enhanced data collaboration, we allow the healthcare analyzers to access both the reencrypted health data and the social data with authorization from the data owner based on proxy reencryption. Specifically, most of the health data encryption and decryption computations are outsourced from resource-constrained mobile devices to a health cloud, and the decryption of the healthcare analyzer incurs a low cost. The security and performance analysis results show the security and efficiency of our scheme.

Exploiting software-defined networking techniques, randomly and instantly mutating routes can disguise strategically important infrastructure and protect the integrity of data networks. Route mutation has been to date formulated as NP-complete constraint satisfaction problem where feasible sets of routes need to be generated with exponential computational complexities, limiting algorithmic scalability to large-scale networks. In this paper, we propose a novel node-centric route mutation method which interprets route mutation as a signature matching problem. We formulate the route mutation problem as a three-dimensional earth mover’s distance (EMD) model and solve it by using a binary branch and bound method. Considering the scalability, we further propose that a heuristic method yields significantly lower computational complexities with marginal loss of robustness against eavesdropping. Simulation results show that our proposed methods can effectively disguise key infrastructure by reducing the difference of historically accumulative traffic among different switches. With significantly reduced complexities, our algorithms are of particular interest to safeguard large-scale networks.

Abstract With the purpose of ensuring secure communication through wireless environments, authenticated key agreement protocols with user anonymity are widely investigated. Inspired by the semi‐group property of Chebyshev maps and multiple servers in the network environment, Tsai et al. proposed a novel chaotic maps‐based anonymous authenticated key agreement protocol based on multiserver architecture. Unfortunately, we observe that the Tsai et al. protocol falls to key‐compromise impersonation attack, which opens the door for an attacker to launch an offline password‐guessing attack. Moreover, the Tsai et al. protocol also unfortunately violates the session key security. Elaborating on the security of chaotic maps‐based authenticated key agreement, we present an enhanced protocol employing biometrics that attempts to repair the security pitfalls found in Tsai et al. Security analysis shows that the enhanced protocol satisfies more security attributes while retaining the merits of the original protocol. We also present a formal proof of the enhanced protocol with the Burrows–Abadi–Needham logic. The performance of our protocol is evaluated with its predecessor protocols, and the comparative results show that it outperforms the predecessor protocols in terms of better trade‐off between desirable security attributes and computational overhead. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract With the greatest advancement of information technology, mobile communication has become more widespread and prevalent. When a mobile user intends to enjoy the services offered by a remote server, he needs to be authenticated before constructing a session key with the corresponding server. Numerous authentication schemes have been provided with the purpose of validating the legitimacy of a mobile user. Recently, Xieet al.presented a modified two‐factor authenticated key exchange to eliminate the security flaws of Chenet al.Xieet al.claimed that the enhanced design was more secure than the design of Chenet al.Unfortunately, we identified that the proposed scheme by Xieet al.was insecure against user impersonation, insider and trace attacks and did fail to provide verification in login phase. To enhance the security and efficiency, we then proposed an anonymous authenticated key exchange scheme for mobile client‐server environment. We demonstrated that the proposed scheme was immune to many attacks including attacks observed in the scheme of Xieet al.We also use a formal proof, namely Burrows–Abadi–Needham logic, to analyze the proposed scheme. In addition, the proposed scheme possesses a lower computation overheads than the other related schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract A digital signature scheme allows a signer to sign electronic messages using his or her secret key, and any verifier can validate the correctness according to a given verification procedure. Although a variety of lattice‐based signature schemes have been proposed in the past few years, there does not exist a scheme that has short signatures and constant‐size public keys simultaneously. In this paper, we propose a new method for constructing short lattice signatures with constant‐size public keys in the standard model. In our scheme, each signature contains a low‐dimensional lattice vector and the public key only contains three matrices plus a vector. Compared with previous constructions, our scheme is very simple and does not require any complex homomorphic computation. In order for security proof to work, we introduce a new hard lattice problem, called variant small integer solution (Variant‐SIS), and give the security reduction from small integer solution to Variant‐SIS. Then we define a family of hash functions based on the hardness of Variant‐SIS and prove its security properties, including one‐wayness and collision resistance. As a matter of independent interest, the proposed hard problem may be useful in many other lattice‐based cryptographic constructions. Last, the comparison with similar works demonstrates the superiority of our scheme. Copyright © 2016 John Wiley & Sons, Ltd.

With the rapid development of computer networks, multi-server architecture has attracted much attention in many network environments. Moreover, in order to achieve non-repudiation which both passwords and cryptographic keys cannot provide, several password authentication schemes combining a user's biometrics for multi-server environments have been proposed in the past. In 2014, Chuang et al. presented a biometrics-based multi-server authenticated key agreement scheme and declared that their scheme was efficient and secure. Later, Mishra et al. commented that the scheme by Chuang et al. was susceptible to stolen smart card, impersonation and denial of service attacks. To conquer these weaknesses, Mishra et al. presented an efficient biometrics-based multi-server authenticated key agreement scheme using hash functions. However, we prove that the scheme by Mishra et al. is insecure against forgery, server masquerading and lacks perfect forward secrecy. The focus of this paper is to present a robust biometrics and public-key techniques-based authentication scheme, which is a significant enhancement to the scheme recently proposed by Mishra et al. The highlight of our scheme is that it not only conquers the flaws but also is efficient compared with other related authenticated key agreement schemes. Copyright © 2015John Wiley & Sons, Ltd.

Abstract Signcryption as a cryptographic primitive can carry out signature and encryption simultaneously at a remarkably reduced cost. Identity‐based cryptography is more convenient than public key infrastructure‐based cryptography in certificate management. As a result, identity‐based signcryption has been studied extensively, and many efficient and provably secure constructions have been proposed. However, most of these schemes are based on intractability assumptions from number theory, and these assumptions have been threatened by the booming quantum computation. Therefore, a recent trend in cryptography is to construct cryptosystems that are based on lattice‐based intractability assumptions because of their plausible features of quantum attack resistance. In this paper, several identity‐based signcryption schemes from lattice hardness assumptions are proposed. In the standard model, these schemes are indistinguishable againstinneradaptively chosen ciphertext attacks (IND‐CCA2) and strongly unforgeable againstinnerchosen message attacks. In our construction, it does not matter whether the original encryption scheme used to construct signcryption is deterministic or probabilistic; the resulted signcryption schemes can reach IND‐CCA2 security. To achieve this, we carefully combine three techniques—the identity‐based encryption from lattice due to Agrawal–Boneh–Boyen (EUROCRYPT 2010), the framework of lattice‐based short signature due to Boyen (Public Key Cryptography 2010), and the Canetti–Halevi–Katz (abbr. CHK) technique, with necessary and tailored optimization—for transforming an (ℓ+ 1)‐level indistinguishable under chosen plaintext attack secure hierarchical identity‐based encryption (HIBE) into anℓlevel IND‐CCA2 secure HIBE scheme. In addition, our security proof also contains a more efficient simulation tool that might have separate interest in cryptographic applications. Copyright © 2015 John Wiley & Sons, Ltd.

In threshold public-key encryption (TPKE), the decryption key is divided into n shares, each one of which is given to a different decryption user in order to avoid single points of failure. A robust TPKE is that if threshold decryption of a valid ciphertext fails, the combiner can identify the decryption users that supplied invalid partial decryption shares. In this paper, we propose a practical and efficient TPKE scheme which is robust and non-interactive. Security against chosen-ciphertext attacks (CCAs) can be proved in the standard model under the hashed Diffie–Hellman assumption in bilinear groups. The security reduction is tight and simple. We use an instantiation hash function of the Kiltz's key encapsulation mechanism and Lai et al.'s chosen-ciphertext secure technique to construct a TPKE scheme. Moreover, our scheme is more simple and shown to be more efficient than currently existing CCA-secure TPKE schemes.

In this paper, we build a family of chameleon hash functions and strongly unforgeable one-time signature schemes based on the intractability assumption of the discrete logarithm problem (DLP) over inner automorphism groups. Since the DLP assumption over inner automorphism groups does not admit sub-exponential attacks, thus the sizes of the working parameters used in our constructions are shorten significantly. This leads to remarkable gains for our proposals both in running time and in storage space. In addition, as far as we know, this is the first time to build CHF and OTS based on noncommutative groups.

ABSTRACT Two novel public key encryption schemes based on the non‐Abelian factorization problems were proposed. Both of them are proved to be indistinguishable against adaptively chosen ciphertext attack (IND‐CCA2) in the random oracle models. These constructions have the potential to resist Shor's quantum algorithm attack proposed in 1994 and give affirmative answers for the open question announced by Myasnikov, Shpilrain and Ushakov in 2011. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Delay tolerant networks (DTNs) are new emerging technologies aiming to solve communication issues in challenged network environments. In such networks, any real‐time interactive key agreement protocol does not work due to the intermittent connectivity and long time delay in message round trips. In the context of specific applications, such as single hop authentication, manual public key exchange is the most direct method because single hop authentication can be achieved by holding a small part of node's public key. To evaluate how many public keys should be maintained by each node to achieve a high propagation speed while single hop authentication scheme is used, in this paper, we proposed a security message propagation model for DTNs formed by vehicles where the extended graph theory and rumor spreading terminology in complex networks were harnessed. We find that holding 8–10 public keys by each node is optimal. And decay rate threshold is 0.16 under which message can be disseminated throughout the whole network. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT We propose a new public‐key cryptosystem named conjugacy search problem‐based Diffie–Hellman integrated encryption scheme (CSP‐DHIES), by using conjugation‐related assumptions for a special monoid of matrices of truncated multi‐variable polynomials over the ring ℤ12where the CSP is assumed to be intractable. Our construction can be viewed as the first noncommunicative variant of the well‐known DHIES cryptosystem. Under the assumptions of the intractability of the CSP‐based hash Diffie–Hellman problem and the CSP‐based oracle Diffie–Hellman problem, our scheme is provably secure against both chosen‐plaintext attacks and secure against chosen‐ciphertext attacks. Our proofs are constructed in the standard model. We also discuss the possibility of implementing our proposal using braid groups. Copyright © 2011 John Wiley & Sons, Ltd.

Cyclic codes with two zeros and their dual codes as a practically and theoretically interesting class of linear codes have been studied for many years and find many applications. The determination of the weight distributions of such codes is an open problem. Generally, the weight distributions of cyclic codes are difficult to determine. Utilizing a class of elliptic curves, this paper determines the weight distributions of dual codes ofq-ary cyclic codes with two zeros for a few more cases, whereqis an odd prime power.

Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a nonlinear and nonconvex optimization problem with prohibitively high computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model. Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to seven-fold speedup over the optimal algorithm.

In recent years network coding has attracted significant attention in telecommunication. The benefits of network coding to a communication network include the increased throughput as well as secure data transmission. The purpose of this work is to design secure linear network coding against wiretap attack. The problem is to maximize the transmission data rate of multiple unicast streams between a pair of source and destination nodes, under the condition of satisfying the weakly secure requirements. Different from most existing research on network coding that designs the network coding scheme based on a given network topology, we will consider the integrated network topology design and network coding design. Such an integrated approach has not been reported by other researchers. In this paper, we formally introduce the problem, prove the problem is computational intractable, and then develop efficient heuristic algorithms. We first try to find the transmission topology that is suitable for network coding. Based on the topology, we design linear network coding scheme that is weakly secure. We conduct simulations to show that the proposed algorithms can achieve good performance.

In CT-RSA 2008, Yu et al. proposed a family of three efficient Online/Offline signature schemes, which are especially suitable for the devices with limited computing capabilities. In this paper, we propose a new security model of Online/Offline signature. We find that Yu et al.' basic scheme is insecure under our model. We repair Yu et al.'s loophole by proposing a modified scheme.

Many remote authentication schemes attempt to preserve user anonymity from the eavesdropper. However, authentication schemes in many e-commerce transactions require not only anonymous to the eavesdropper but also to the authentication server. In this paper, we propose a remote authentication scheme using smart card to fulfill both of the requirements. The proposed scheme achieves mutual authentication and allows users to choose and change their own passwords freely and securely. We only use one-way hash function and bitwise XOR operation in the proposed scheme and so the scheme has a low computational complexity. By using timestamp and random numbers, the proposed scheme can resist the denial of service attack and the replay attack. Also, the scheme is secure against guessing attack, insider attack, stolen-verifier attack, reflection attack, and impersonation attack.

We consider the multi-valued discrete real training set that can not be separated by one multi-valued multi-threshold neuron. Such training set is defined as linearly nonseparable set in this paper. Our objective is to use multi-valued multi-threshold neural networks to learn nonseparable training sets. First we give the method that how to determine a training set is separable or nonseparable (i.e., the necessary and sufficient condition for linearly nonseparable is given). Then we analyze the structures within linearly nonseparable sets: not all the vectors in a linearly nonseparable set are responsible for nonseparability. So the vectors in such set can be partitioned to separable vectors and nonseparable vectors. Finally, we discuss the learning problems for a linearly nonseparable set. Such set can be learned by a three-layer feedforward neural network with one hidden layer. An example throughout the paper further clarifies the results of this paper.

Current certificate-based information security technologies are facing a challenge of lacking the exact connection between cryptographic key and legitimate users and a problem of comprehensive management of certificates. In this paper we propose a novel networking identity authentication scheme based on fingerprint coding method and Identity-based encryption (IBE). Fingerprint code is a dual-factor authenticator which combines a token, e.g. USB key, with user's fingerprint feature by direct mixing of pseudo-random number generated by token and fingerprint feature. IBE allows for a sender to encrypt a message to a receiver without access to a public key certificate. This scheme can improve the security of user authentication and simplify comprehensive management of certificates, in the meantime, protect against fingerprint data fabrication and stolen. The security and efficiency of our proposed scheme meet the requirements of practical applications.

High-speed, large-scale networks present new challenges to an intrusion detection system. These challenges include: the volume of data that must be analyzed, the high-speed data stream that IDS must deal with. To adapt these new demands, this paper propose a novel architecture for Large-scale Distributed Intrusion Detection Systems(LDIDS) that can be applied to large-scale networks. This architecture is based on hierarchy, which consists of a root node, several branch nodes and leaf nodes. In this architecture, each node is an independent IDS, all IDSs constituting the whole LDIDS. The main advantage of this architecture is scalability and collaboration. We describe the framework of the nodes in detail. We also present an implementation of LDIDS which is designed according to the architecture.

The spatial LSB steganography results in the alteration of the smooth characteristics between adjoining pixels of the raw image. The relation between the length of embedded message and the gradient energy is theoretically analyzed, and then a steganalysis and detection method, named gradient energy-flipping rate detection (GEFR) is proposed. Through the analysis of the variation of the gradient energy, which results from the LSB steganography in color and grayscale image, the secret message embedded in the target image is detected, and the length of the embedded message is estimated. The method is proved effective and accurate by simulation (detection rate reaches 0.01bit per pixel).

In this correspondence, we study families of generalized geometric sequences formed bp applying a feedforward function to certain sums of decimated m-sequences with elements in a finite field. We compute their correlation functions, which for certain families turn out to be close to the square root of the period. The size of these families equals their period. We also show that in the binary case, the linear complexities of these sequences are much larger than those of cascaded geometric sequences, although in these cases the maximum correlations are larger.

In the literature, several "divisible" untraceable offline electronic cash schemes have been presented. An efficient single-term divisible electronic cash based on bit commitment scheme and one-time knowledge proof is proposed. The complexity of every protocol in our scheme is O(log N), where N is the precision of divisibility, i.e., N=(the total coin value)/(minimum divisible unit value). This scheme overcomes the problems of previous schemes through its greater efficiency and clarified assumptions.

For original paper see Hwang and Ku (IEEE Trans. Commun., vol.43, p.1947-9, 1995 May). The present authors find that the key distribution protocol KDP2 proposed in the original paper is, in fact, not reparable, as was claimed by the original authors. A reparable revised version of this KPD2 is then introduced. In a reply Hwang presents an example to show that the Lin et al. revised reparable KDP2 did not solve the problem which they proposed.

The following results are proven in this paper: 1) neither periodic autonor cross-correlation of the truncated Costas optical orthogonal code (TC OOC) is upper bounded by 1; 2) TC OOC is a class of (/spl omega/(2p-3), /spl omega/,2,2) optical orthogonal codes.

The undetected error probability (UEP) of binary (n, 2/spl delta/, m) nonlinear constant weight codes over the binary symmetric channel (BSC) is investigated, where n is the blocklength, m is the weight of codeword and 2/spl delta/ is the minimum distance of the codes. The distance distribution of the (n, 2, m) nonlinear constant weight codes is evaluated. It is proven in this paper that the (5, 2, 2) code, (5, 2, 3) code, (6, 2, 3) code, (7, 2, 4) code, (7, 2, 3) code and (8, 2, 4) code are the only proper error-detecting codes in the (n, 2, m) nonlinear constant weight codes for n/spl ges/5, in the sense that their UEP is increased monotonically with the channel error rate p, of course all these proper codes are m-out-of-n codes. Furthermore, it is conjectured that except for the cases of n/spl les/4/spl delta/, there are no proper error-detecting binary (n, 2/spl delta/, m) nonlinear constant weight codes, for n>8 and /spl deltaspl ges/1.>