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2023  Vol. 60  No. 12

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Information Security
Abstract:

MIBS is a lightweight block cipher which was proposed by Izadi et al. at CANS 2009. Its overall encryption structure uses the typical Feistel network, and the round function adopts the SP network. MIBS supports both MIBS-64 and MIBS-80 versions, that is, it has 64-bit and 80-bit two key lengths with a 64-bit block size, and is suitable for strictly resource-constrained devices, such as low-cost RFID (radio frequency identification) tags. We study the integral attack on the block cipher MIBS. Firstly, we observe the key schedules of MIBS-64 and MIBS-80, and find some properties between their round keys by using the automatic search algorithm for key-bridging technique, respectively. Secondly, using the bit-based division property and the automatic modeling search method based on MILP (mixed integer linear programming), we find some 8-round and 9-round integral distinguishers of MIBS. Then, based on the 8-round integral distinguisher, we launch a 12-round key recovery attack for MIBS-64 with the data complexity 260, and the time complexity 263.42. Finally, based on the 9-round integral distinguisher, we launch a 14-round key recovery attack for MIBS-80 with the data complexity 263, and the time complexity 266. These two key recoveries are the current best integral attacks on the block cipher MIBS-64 and MIBS-80.

Abstract:

PEKS (public key encryption with keyword search) enables users to search over encrypted data stored in the untrusted cloud server, which is of great significance for data privacy protection and is of increasing interest for this reason. PAEKS (public key authenticated encryption with keyword search) requires that a data sender not only uses the receiver’s public key to encrypt the keyword, but also uses his own private key to authenticate the keyword. PAEKS ensures that the adversaries cannot construct a keyword ciphertext, thus resisting the keyword guessing attacks (KGAs) that PEKS is facing. In this paper, we propose a scheme for public key authenticated encryption with keyword search based on SGX (software guard extensions), which supporting searching on encrypted data by creating a trusted zone and running a keyword comparison enclave program in the cloud server. The formal security proof of the scheme is provided and shows that the scheme satisfies the ciphertext indistinguishability and trapdoor indistinguishability, that is, the scheme can resist keyword guessing attacks. Further, the search pattern privacy (SP-Privacy) is defined, which ensures that adversaries cannot judge whether two searches are the same keyword only through the trapdoors, so as to avoid revealing some privacy to external adversaries. In addition, the scheme can be easily extended to support complicated search functionalities and enhance privacy protection, e.g. forward security. As examples, brief descriptions about how to extend the scheme to support multi-keyword search, search capability sharing, as well as forward security are given. Experiments in real scenario show the better efficiency of the scheme compared with some other typical schemes.

Abstract:

DSSE (dynamic searchable symmetric encryption), which has forward/backward privacy and high search efficiency, supports addition and deletion of encrypted index. Relevant research has been a hot spot and many new schemes have been constructed in recent years, such as Aura. Aiming at the problems of high ciphertext storage overhead and mistaken deletion in Aura scheme, a more strict correctness definition of SRE (symmetric revokable encryption) primitive is given, and the condition of mistaken deletion is analyzed theoretically. In addition, an insertion position selection algorithm is designed to avoid node reuse due to Hash collision. On this basis, a searchable symmetric encryption scheme based on SRE is constructed by adding a deletion list and using a t-puncturable pseudorandom function. Puncturing all unused nodes at one time, the scheme not only effectively reduces the computing overhead on the cloud server during search phase, but also avoids revealing unused keys and gains better security. Finally, the scheme is analyzed in terms of search efficiency, storage overhead, communication overhead and security. Theoretical analysis and experimental results show that the scheme can effectively reduce the space overhead of ciphertext storage on the server, avoid the mistaken deletion, and improve the search efficiency on large-scale nodes.

Abstract:

In the attribute-based signature (ABS) scheme, the secret key of the signer is generated by attribute authority with different attributes, and the signature can be generated successfully only when the attributes meet the given signing policy. The verifier does not need to know the identity of the signer to determine whether the signature is valid. As a result, ABS has attracted wide attention due to its anonymity and fine-grained access control. In ABS scheme, once the key leakage occurs, the attacker can use the leaked key to generate a valid signature. The original message often contains some sensitive information. For example, in e-health or electronic finance scenario, personal privacy information is contained in personal medical records or transaction records. If the original message is not desensitized, sensitive personal information will be leaked. In order to solve the problems of key leakage and sensitive information leakage, an efficient and forward-secure attribute-based sanitizable signature (FABSS) scheme is proposed. The security of FABSS is reduced to the η-DHE (η-Diffie-Hellman exponent) assumption problem under the standard model. The proposed scheme not only protects signer privacy and supports fine-grained access control, but also has the ability to hide sensitive information and resist key leakage. In addition, the length of signature is constant, and only a constant number of pairing operations need to be calculated in the verification stage. Experimental analysis shows that the performance of the proposed scheme is efficient.

Network Technology
Abstract:

From smart terminal devices such as smart phones and smart watches, to large-scale intelligent applications, such as smart homes, Internet of vehicles, intelligent life and intelligent agriculture. Artificial intelligence (AI) has gradually entered and changed the life of human being. In this context, various of intelligent devices have produced massive amount of data, making traditional cloud computing paradigm unable to adapt to the unprecedented challenge. Instead, edge computing which aims to process the data at the edge of the network has the great potential to reduce latency and bandwidth pressure, as well as protect data privacy and security. Building AI models upon edge computing architecture, training and inferring the model, realizing the intelligence of the edge are crucial to the current social. As a result, a new interdisciplinary field, edge intelligence (EI), has begun to attract widespread attention. We make a comprehensive study on EI. Specifically, firstly introduce the basic knowledge of edge computing and AI, which leads to the background, motivation and challenges of EI. Secondly, the research on EI related technologies is discussed from three aspects, namely, the problems, the models and the algorithm. Further, the typical security problems in EI are introduced. Next, the applications of EI are described from three aspects of intelligent industry, intelligent life and intelligent agriculture. Finally, we propose the direction and prospect of EI in the future development.

Abstract:

Edge computing deploys computing and storage resources on the edge of the network closed to users, so that users can offload high-latency and energy-intensive applications to the edge of the network for execution to reduce application latency and local energy consumption. Existing offloading research usually assumes that the offloaded tasks are independent of each other, and the edge server caches all the services required for task execution. However, in real scenarios, there are often dependent between tasks, and edge servers can only cache limited services due to their limited storage resources. To this end, we propose a dependent task offloading method that balances latency and energy consumption (i.e., cost) under the constraints of limited computing resources and service caches on edge servers. First, the constraints in the research problem are relaxed to be transformed into a convex optimization problem. A convex optimization tool is used to find the optimal solution, which is used to calculate the priority of offloading tasks. Then, the tasks are offloaded to the edge server with the least cost according to the priority. If multiple dependent tasks are offloaded to different edge servers, an improved particle swarm optimization is used to solve the optimal transmission power of edge servers to minimize the total cost. Finally, sufficient experiments are performed based on real datasets to verify the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the total cost by approximately 8% to 23% compared with other methods.

Abstract:

A controller area network (CAN) bus protocol is widely used in the vehicular system and is an efficient standard bus enabling communication between all electronic control units (ECUs). However, the CAN bus is easy to be attacked because of a lack of security defense features. We propose self-attention mechanism (SAM) enhanced grid long short-term memory (Grid LSTM) for vehicular intrusion detection, namely SALVID. The SAM can enhance the characteristics of CAN bus-oriented attack behavior, and the Grid LSTM can effectively extract the depth features of time series data. We generate five attack datasets by extracting benign CAN data from the actual car, including denial of service (DoS), fuzzy, spoofing, replay, and delete attacks. We compare the performance of various models with different model depths, and the results demonstrate that SALVID has the best performance in detecting the attacks on CAN bus. SALVID can identify attacks with small-batch features according to an overall detection accuracy of 98.98%, which is hard to be done in previous studies. We also design and implement SALVID based on field programmable gate array (FPGA) embedded platform and use parallel optimization and quantification to accelerate the model based on previous experiments. Even with a certain degree of quantification, SALVID still displays high detection accuracy of 98.81% and a latency of 1.88 ms. The investigation provides a new idea for designing high-performance and real-time vehicular intrusion detection systems.

Abstract:

The intelligent traffic signal control system is a component of the intelligent traffic system (ITS), offering real-time services for the creation of a safe and efficient traffic environment. However, due to restricted communication, conventional adaptive traffic signal-controlled methods are unable to fulfill the complex and changing traffic requirements. A multi-agent adaptive coordination method (ADM) based on asynchronous decision-making and edge computing is presented to address the issues of communication delay and a decrease in signal utilization. Firstly, the end-side-cloud architecture is proposed for real-time environmental information collection and related processing. Then, to enhance the agent coordination process, asynchronous communication is implemented. An approach for calculating the decision cycle of the agent is presented, and an asynchronous decision mechanism employing multiple agents’ decision cycles is devised. The experimental results show that edge computing technology provides a good solution for traffic signal control scenarios with high real-time requirements. In addition, compared with the fixed time (FT) and independent Q-learning decision algorithm (IQA), ADM achieves collaboration among the agents based on the asynchronous decision mechanism and the neighbor information base, and reduces the average vehicle waiting length and improves intersection time utilization.

Artificial Intelligence
Abstract:

Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances. Recently, there is a growing interest in studying pairwise learning since it includes many important machine learning tasks as specific examples, e.g., metric learning, AUC maximization and ranking. Regret bounds are particularly important for generalization analysis of online pairwise learning. The existing online pairwise learning analysis provides regret bounds only with convex loss functions. To fill the gap in the theoretical study of online pairwise learning with non-convex loss functions, we present a systematic study on the generalization analysis for online pairwise learning and propose regret bounds for non-convex online pairwise learning in this paper. We consider online learning in an adversarial, non-convex setting under the assumption that the learner has access to an offline optimization oracle and the learner’s prediction with expert advice. We first propose a general online pairwise learning framework and establish the stability of online pairwise learning with non-convex loss functions. Then, the regret bounds can be derived naturally from stability. Finally, we show that the general online pairwise learning framework with non-convex loss functions achieves optimal regret bounds of O(T1/2) when the learner has access to an offline optimization oracle.

Abstract:

Painting is an important form of culture and art. For thousands of years, a large number of paintings have been produced in ancient China. And paintings contain rich cultural, artistic, scientific, and historical values. But due to natural disasters (earthquake) and various reasons such as natural weathering and more and more human economic activities, some paintings are more or less damaged or missing in large pieces, which seriously affects the appreciation, cultural creativity, cultural communication and other activities based on these paintings. Compared with natural images, ancient painting images usually have high self-similarity, obvious style characteristics, rich cultural connotation and delicate texture. Although impressive progress has been made in the inpainting technology of natural images, these methods cannot be directly applied to the inpainting of ancient Chinese paintings. Combined with the characteristics of ancient Chinese paintings, we design the algorithm and model structure and proposes a Chinese ancient painting inpainting algorithm based on a multi-channel encoder and dual attention module. The goal is to automatically repair the damaged ancient paintings. In order to better repair the ancient paintings from multiple scales, we use a multi-channel encoder to learn the semantic features of ancient paintings at different scales and repair ancient paintings through the learned macro, meso, and micro semantic features, which solves the difficult problem of rich and delicate texture repair of ancient paintings. In order to better learn the global semantic features of ancient paintings and conduct the harmony and consistency of the repaired ancient paintings, we use the dual attention module to learn the global semantic features of ancient paintings from two aspects: style and content. In order to verify the advanced nature of the algorithm, an ancient painting data set is produced. Experiments on this dataset prove that the algorithm proposed in this paper has better repair quality than the SOTA algorithms.

Abstract:

Community discovery aims to uncover the community structure embedded in complex networks, which is one of the important tasks in complex network analysis. However, most of the existing community discovery methods are aimed at single-layer network data, and less research has been done on the widespread multi-layer networks in the real world. In order to solve the problem of community discovery in multi-layer networks, we propose a two-stage ensemble-based community discovery algorithm. The algorithm can improve the accuracy and interpretability of community discovery results. Firstly, after the base communities are obtained at each layer, the local ensemble is performed based on the base community structure information of each layer and the optimal base community structure of each other layer. Secondly, the stability of each community in the local community division of each layer is measured based on information entropy, and the accuracy of each local community division is evaluated through the results of other layer community division. Finally, a global weighted ensemble is carried out based on the importance of each community and the community structure to obtain the final community structure. A comparative analysis is carried out on artificial multi-layer networks and real multi-layer networks with existing multi-layer network community discovery algorithms. The experimental results show that our proposed algorithm is superior to the existing algorithms in terms of multi-layer modularity and normalized mutual information.

Abstract:

The deep neural network has been widely used in natural language processing. In text generation tasks with multi-domain data, there is often a discrepancy of data in different domains. And the introduction of new domains can simultaneously bring about the problem of data deficiency. The supervised methods require a large amount of data containing ground-truth in the domain of the task to train a deep neural network text generation model, and the trained model cannot achieve good generalization in a new domain. To address the problems of data distribution differences and data deficiency in multi-domain tasks, a comprehensive transfer text generation method inspired by transfer learning methods is designed to reduce the data distribution differences in text data between different domains while leveraging the semantic correlation on text data between source domain and target domain to help deep neural network text generation models generalize over new domains. The effectiveness of the proposed method for domain transfer is verified through experiments on a publicly available dataset, and the transfer deep neural network text generation model has a better performance in text generation on new domains. Also, the proposed method improves in all text generation evaluation metrics compared with other existing transfer text generation methods.

Abstract:

Cross-domain named entity recognition aims to alleviate the problem of insufficient annotation data in the target domain. Most existing methods, which exploit the feature representation or model parameter sharing to achieve cross-domain transfer of entity recognition capabilities and can only partially utilize structured knowledge entailed in text sequences. To address this, we propose a multi-level structured semantic knowledge enhanced cross-domain named entity recognition MSKE-CDNER, which could facilitate the transfer of entity recognition capabilities by aligning the structured knowledge representations embedded in the source and target domains from multiple levels. First, MSKE-CDNER uses the structural feature representation layer to achieve structured semantic knowledge representations of texts from different fields’ structured alignment. And then, these structured semantic representations are aligned at the corresponding layers by a latent alignment module to obtain cross-domain invariant knowledge. Finally, this cross-domain consistent structured knowledge is fused with domain-specific knowledge to enhance the generalization capability of the model. Experiments on five datasets and a specific cross-domain named entity recognition dataset have shown that the average performance of MSKE-CDNER improved by 0.43% and 1.47% compared with the current models. All of these indicate that exploiting text sequences’ structured semantic knowledge representation could effectively enhance entity recognition in the target domain.

Abstract:

Aspect-term extraction (AE) and aspect-level sentiment classification (ALSC) extract aspect-sentiment pairs in the sentence, which helps social media platforms such as Twitter and Facebook to mine users’ sentiments of different aspects and is of great significance to personalized recommendation. In the field of multimodality, the existing method uses two independent models to complete two subtasks respectively. Aspect-term extraction identifies goods, important people and other entities or entities’ aspects in the sentence, and aspect-level sentiment classification predicts the user’s sentiment orientation according to the given aspect terms. There are two problems in the above method: first, using two independent models loses the continuity of the underlying features between the two tasks, and cannot model the potential semantic association of sentences; second, aspect-level sentiment classification can only predict the sentiment of one aspect at a time, which does not match the throughput of aspect-term extraction model that extracts multiple aspects simultaneously, and the serial execution of the two models makes the efficiency of extracting aspect-sentiment pairs low. To solve the above problems, a unified framework based on multimodal aspect-term extraction and aspect-level sentiment classification, called UMAS, is proposed in this paper. Firstly, the shared feature module is built to realize the latent semantic association modeling between tasks, and to make the two subtasks only need to care about their upper network, which reduces the complexity of the model. Secondly, multiple aspects and their corresponding sentiment categories in the sentence are output at the same time by using sequence tagging, which improves the extraction efficiency of aspect-sentiment pairs. In addition, we introduce part of speech in two subtasks at the same time: using the grammatical information to improve the performance of aspect-term extraction, and the information of opinion words is obtained through part of speech to improve the performance of aspect-level sentiment classification. The experimental results show that the unified model has superior performance compared with multiple baseline models on the two benchmark datasets of Twitter2015 and Restaurant2014.

Software Technology
Abstract:

Given a collection of sets, the set similarity self-join (SSSJ) algorithm finds all pairs of sets whose similarity is higher than a given threshold, which has been widely used in various applications. The SSSJ adopting the filter-and-verification framework and the parallel and distributed computing framework MapReduce is an active research topic. But existing algorithms produce large candidate sets when the given threshold is low and lead to poor performance. To address this problem, we propose to compute the SSSJ with frequent pattern tree structures and their derivatives FP-tree*, which are used to compress data in memory for computation, targeting at reducing the size of candidate sets. First, based on an investigation on existing FP-tree* and the characteristics of SSSJ computation, we propose a more traversal efficient linear prefix tree structure, i.e., TELP-tree, and an original SSSJ algorithm accordingly, i.e., TELP-SJ, which includes a two-phase filtering strategy: tree-construction oriented filtering algorithm and tree-traversal oriented filtering strategy. These algorithms will reduce the size of TELP-trees and tree-traversals. Then, we design the parallel and distributed SSSJ computation algorithm FastTELP-SJ with MapReduce. Finally, 4 groups of empirical comparison studies have been carried out on 4 sets of real application datasets. The experimental results demonstrate that FastTELP-SJ achieves better performance in term of the execution time, memory usage, disk usage and scalability for similarity joins over large scale collections of sets with high dimension.

Abstract:

SLP (superword level parallelism) is an efficient auto-vectorization method to exploit the data level parallelism for basic block, oriented to SIMD (single instruction multiple data), and SLP has been widely used in the mainstream compilers. SLP performs vectorization by finding multiple sequences of isomorphic instructions in the same basic block. Recently there is a research trend that the compilers translate the sequences of non-isomorphisic instructions into the sequences of isomorphisic instructions to extend application scope of the SLP vectorization method. In this paper, we introduce SLP-M, a novel auto-vectorization method that can effectively vectorize the code containing sequences of non-isomorphic instructions in the same basic block, translatting the code into isomorphic form by selection and conduction of multiple transformation methods based on condition judgment and benefit evaluation. A new transformation method for binary expression replacement is also proposed. SLP-M improves application scope and performance benefit for SLP. We implement SLP-M in LLVM. A set of applications are taken from some benchmarks such as SPEC CPU 2017 to compare our approach and prior techniques. The experiments show that, compared with the existing methods, the performance of SLP-M improves by 21.8% on kernel functions, and improves by 4.1% in the overall tests of the benchmarks.