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1
2016, 53(3): 582-600.
DOI: 10.7544/issn1000-1239.2016.20148228
Abstract：
Google’s knowledge graph technology has drawn a lot of research attentions in recent years. However, due to the limited public disclosure of technical details, people find it difficult to understand the connotation and value of this technology. In this paper, we introduce the key techniques involved in the construction of knowledge graph in a bottom-up way, starting from a clearly defined concept and a technical architecture of the knowledge graph. Firstly, we describe in detail the definition and connotation of the knowledge graph, and then we propose the technical framework for knowledge graph construction, in which the construction process is divided into three levels according to the abstract level of the input knowledge materials, including the information extraction layer, the knowledge integration layer, and the knowledge processing layer, respectively. Secondly, the research status of the key technologies for each level are surveyed comprehensively and also investigated critically for the purposes of gradually revealing the mysteries of the knowledge graph technology, the state-of-the-art progress, and its relationship with related disciplines. Finally, five major research challenges in this area are summarized, and the corresponding key research issues are highlighted.
Google’s knowledge graph technology has drawn a lot of research attentions in recent years. However, due to the limited public disclosure of technical details, people find it difficult to understand the connotation and value of this technology. In this paper, we introduce the key techniques involved in the construction of knowledge graph in a bottom-up way, starting from a clearly defined concept and a technical architecture of the knowledge graph. Firstly, we describe in detail the definition and connotation of the knowledge graph, and then we propose the technical framework for knowledge graph construction, in which the construction process is divided into three levels according to the abstract level of the input knowledge materials, including the information extraction layer, the knowledge integration layer, and the knowledge processing layer, respectively. Secondly, the research status of the key technologies for each level are surveyed comprehensively and also investigated critically for the purposes of gradually revealing the mysteries of the knowledge graph technology, the state-of-the-art progress, and its relationship with related disciplines. Finally, five major research challenges in this area are summarized, and the corresponding key research issues are highlighted.
2
2016, 53(2): 247-261.
DOI: 10.7544/issn1000-1239.2016.20160020
Abstract：
Knowledge bases are usually represented as networks with entities as nodes and relations as edges. With network representation of knowledge bases, specific algorithms have to be designed to store and utilize knowledge bases, which are usually time consuming and suffer from data sparsity issue. Recently, representation learning, delegated by deep learning, has attracted many attentions in natural language processing, computer vision and speech analysis. Representation learning aims to project the interested objects into a dense, real-valued and low-dimensional semantic space, whereas knowledge representation learning focuses on representation learning of entities and relations in knowledge bases. Representation learning can efficiently measure semantic correlations of entities and relations, alleviate sparsity issues, and significantly improve the performance of knowledge acquisition, fusion and inference. In this paper, we will introduce the recent advances of representation learning, summarize the key challenges and possible solutions, and further give a future outlook on the research and application directions.
Knowledge bases are usually represented as networks with entities as nodes and relations as edges. With network representation of knowledge bases, specific algorithms have to be designed to store and utilize knowledge bases, which are usually time consuming and suffer from data sparsity issue. Recently, representation learning, delegated by deep learning, has attracted many attentions in natural language processing, computer vision and speech analysis. Representation learning aims to project the interested objects into a dense, real-valued and low-dimensional semantic space, whereas knowledge representation learning focuses on representation learning of entities and relations in knowledge bases. Representation learning can efficiently measure semantic correlations of entities and relations, alleviate sparsity issues, and significantly improve the performance of knowledge acquisition, fusion and inference. In this paper, we will introduce the recent advances of representation learning, summarize the key challenges and possible solutions, and further give a future outlook on the research and application directions.
3
2013, 50(1): 146-169.
Abstract：
Data type and amount in human society is growing in amazing speed which is caused by emerging new services such as cloud computing, internet of things and social network, the era of big data has come. Data has been fundamental resource from simple dealing object, and how to manage and utilize big data better has attracted much attention. Evolution or revolution on database research for big data is a problem. This paper discusses the concept of big data, and surveys its state of the art. The framework of big data is described and key techniques are studied. Finally some new challenges in the future are summarized.
Data type and amount in human society is growing in amazing speed which is caused by emerging new services such as cloud computing, internet of things and social network, the era of big data has come. Data has been fundamental resource from simple dealing object, and how to manage and utilize big data better has attracted much attention. Evolution or revolution on database research for big data is a problem. This paper discusses the concept of big data, and surveys its state of the art. The framework of big data is described and key techniques are studied. Finally some new challenges in the future are summarized.
4
2015, 52(10): 2373-2381.
DOI: 10.7544/issn1000-1239.2015.20150562
Abstract：
Anonymous communication technique is one of the main privacy-preserving techniques, which has been widely used to protect Internet users’ privacy. However, existing anonymous communication systems are particularly vulnerable to traffic analysis, and researchers have been improving unobservability of systems against Internet censorship and surveillance. However, how to quantify the degree of unobservability is a key challenge in anonymous communication systems. We model anonymous communication systems as an alternating turing machine, and analyze adversaries’ threat model. Based on this model, this paper proposes a relative entropy approach that allows to quantify the degree of unobservability for anonymous communication systems. The degree of unobservability is based on the probabilities of the observed flow patterns by attackers. We also apply this approach to measure the pluggable transports of TOR, and show how to calculate it for comparing the level of unobservability of these systems. The experimental results show that it is useful to evaluate the level of unobservability of anonymous communication systems. Finally, we present the conclusion and discuss future work on measuring unobservability in anonymous communication systems.
Anonymous communication technique is one of the main privacy-preserving techniques, which has been widely used to protect Internet users’ privacy. However, existing anonymous communication systems are particularly vulnerable to traffic analysis, and researchers have been improving unobservability of systems against Internet censorship and surveillance. However, how to quantify the degree of unobservability is a key challenge in anonymous communication systems. We model anonymous communication systems as an alternating turing machine, and analyze adversaries’ threat model. Based on this model, this paper proposes a relative entropy approach that allows to quantify the degree of unobservability for anonymous communication systems. The degree of unobservability is based on the probabilities of the observed flow patterns by attackers. We also apply this approach to measure the pluggable transports of TOR, and show how to calculate it for comparing the level of unobservability of these systems. The experimental results show that it is useful to evaluate the level of unobservability of anonymous communication systems. Finally, we present the conclusion and discuss future work on measuring unobservability in anonymous communication systems.
5
2019, 56(1): 69-89.
DOI: 10.7544/issn1000-1239.2019.20180760
Abstract：
With the burgeoning of the Internet of everything, the amount of data generated by edge devices increases dramatically, resulting in higher network bandwidth requirements. In the meanwhile, the emergence of novel applications calls for the lower latency of the network. It is an unprecedented challenge to guarantee the quality of service while dealing with a massive amount of data for cloud computing, which has pushed the horizon of edge computing. Edge computing calls for processing the data at the edge of the network and develops rapidly from 2014 as it has the potential to reduce latency and bandwidth charges, address the limitation of computing capability of cloud data center, increase availability as well as protect data privacy and security. This paper mainly discusses three questions about edge computing: where does it come from, what is the current status and where is it going? This paper first sorts out the development process of edge computing and divides it into three periods: technology preparation period, rapid growth period and steady development period. This paper then summarizes seven essential technologies that drive the rapid development of edge computing. After that, six typical applications that have been widely used in edge computing are illustrated. Finally, this paper proposes six open problems that need to be solved urgently in future development.
With the burgeoning of the Internet of everything, the amount of data generated by edge devices increases dramatically, resulting in higher network bandwidth requirements. In the meanwhile, the emergence of novel applications calls for the lower latency of the network. It is an unprecedented challenge to guarantee the quality of service while dealing with a massive amount of data for cloud computing, which has pushed the horizon of edge computing. Edge computing calls for processing the data at the edge of the network and develops rapidly from 2014 as it has the potential to reduce latency and bandwidth charges, address the limitation of computing capability of cloud data center, increase availability as well as protect data privacy and security. This paper mainly discusses three questions about edge computing: where does it come from, what is the current status and where is it going? This paper first sorts out the development process of edge computing and divides it into three periods: technology preparation period, rapid growth period and steady development period. This paper then summarizes seven essential technologies that drive the rapid development of edge computing. After that, six typical applications that have been widely used in edge computing are illustrated. Finally, this paper proposes six open problems that need to be solved urgently in future development.
6
2017, 54(10): 2170-2186.
DOI: 10.7544/issn1000-1239.2017.20170471
Abstract：
Core features of the blockchain technology are “de-centralization” and “de-trusting”. As a distributed ledger technology, smart contract infrastructure platform and novel distributed computing paradigm, it can effectively build programmable currency, programmable finance and programmable society, which will have a far-reaching impact on the financial and other fields, and drive a new round of technological change and application change. While blockchain technology can improve efficiency, reduce costs and enhance data security, it is still in the face of serious privacy issues which have been widely concerned by researchers. The survey first analyzes the technical characteristics of the blockchain, defines the concept of identity privacy and transaction privacy, points out the advantages and disadvantages of blockchain technology in privacy protection and introduces the attack methods in existing researches, such as transaction tracing technology and account clustering technology. And then we introduce a variety of privacy mechanisms, including malicious nodes detection and restricting access technology for the network layer, transaction mixing technology, encryption technology and limited release technology for the transaction layer, and some defense mechanisms for blockchain applications layer. In the end, we discuss the limitations of the existing technologies and envision future directions on this topic. In addition, the regulatory approach to malicious use of blockchain technology is discussed.
Core features of the blockchain technology are “de-centralization” and “de-trusting”. As a distributed ledger technology, smart contract infrastructure platform and novel distributed computing paradigm, it can effectively build programmable currency, programmable finance and programmable society, which will have a far-reaching impact on the financial and other fields, and drive a new round of technological change and application change. While blockchain technology can improve efficiency, reduce costs and enhance data security, it is still in the face of serious privacy issues which have been widely concerned by researchers. The survey first analyzes the technical characteristics of the blockchain, defines the concept of identity privacy and transaction privacy, points out the advantages and disadvantages of blockchain technology in privacy protection and introduces the attack methods in existing researches, such as transaction tracing technology and account clustering technology. And then we introduce a variety of privacy mechanisms, including malicious nodes detection and restricting access technology for the network layer, transaction mixing technology, encryption technology and limited release technology for the transaction layer, and some defense mechanisms for blockchain applications layer. In the end, we discuss the limitations of the existing technologies and envision future directions on this topic. In addition, the regulatory approach to malicious use of blockchain technology is discussed.
7
2019, 56(10): 2135-2150.
DOI: 10.7544/issn1000-1239.2019.20190415
Abstract：
Human society is witnessing a wave of artificial intelligence (AI) driven by deep learning techniques, bringing a technological revolution for human production and life. In some specific fields, AI has achieved or even surpassed human-level performance. However, most previous machine learning theories have not considered the open and even adversarial environments, and the security and privacy issues are gradually rising. Besides of insecure code implementations, biased models, adversarial examples, sensor spoofing can also lead to security risks which are hard to be discovered by traditional security analysis tools. This paper reviews previous works on AI system security and privacy, revealing potential security and privacy risks. Firstly, we introduce a threat model of AI systems, including attack surfaces, attack capabilities and attack goals. Secondly, we analyze security risks and counter measures in terms of four critical components in AI systems: data input (sensor), data preprocessing, machine learning model and output. Finally, we discuss future research trends on the security of AI systems. The aim of this paper is to arise the attention of the computer security society and the AI society on security and privacy of AI systems, and so that they can work together to unlock AI’s potential to build a bright future.
Human society is witnessing a wave of artificial intelligence (AI) driven by deep learning techniques, bringing a technological revolution for human production and life. In some specific fields, AI has achieved or even surpassed human-level performance. However, most previous machine learning theories have not considered the open and even adversarial environments, and the security and privacy issues are gradually rising. Besides of insecure code implementations, biased models, adversarial examples, sensor spoofing can also lead to security risks which are hard to be discovered by traditional security analysis tools. This paper reviews previous works on AI system security and privacy, revealing potential security and privacy risks. Firstly, we introduce a threat model of AI systems, including attack surfaces, attack capabilities and attack goals. Secondly, we analyze security risks and counter measures in terms of four critical components in AI systems: data input (sensor), data preprocessing, machine learning model and output. Finally, we discuss future research trends on the security of AI systems. The aim of this paper is to arise the attention of the computer security society and the AI society on security and privacy of AI systems, and so that they can work together to unlock AI’s potential to build a bright future.
8
2013, 50(9): 1799-1804.
Abstract：
Machine learning is an important area of artificial intelligence. Since 1980s, huge success has been achieved in terms of algorithms, theory, and applications. From 2006, a new machine learning paradigm, named deep learning, has been popular in the research community, and has become a huge wave of technology trend for big data and artificial intelligence. Deep learning simulates the hierarchical structure of human brain, processing data from lower level to higher level, and gradually composing more and more semantic concepts. In recent years, Google, Microsoft, IBM, and Baidu have invested a lot of resources into the R&D of deep learning, making significant progresses on speech recognition, image understanding, natural language processing, and online advertising. In terms of the contribution to real-world applications, deep learning is perhaps the most successful progress made by the machine learning community in the last 10 years. In this article, we will give a high-level overview about the past and current stage of deep learning, discuss the main challenges, and share our views on the future development of deep learning.
Machine learning is an important area of artificial intelligence. Since 1980s, huge success has been achieved in terms of algorithms, theory, and applications. From 2006, a new machine learning paradigm, named deep learning, has been popular in the research community, and has become a huge wave of technology trend for big data and artificial intelligence. Deep learning simulates the hierarchical structure of human brain, processing data from lower level to higher level, and gradually composing more and more semantic concepts. In recent years, Google, Microsoft, IBM, and Baidu have invested a lot of resources into the R&D of deep learning, making significant progresses on speech recognition, image understanding, natural language processing, and online advertising. In terms of the contribution to real-world applications, deep learning is perhaps the most successful progress made by the machine learning community in the last 10 years. In this article, we will give a high-level overview about the past and current stage of deep learning, discuss the main challenges, and share our views on the future development of deep learning.
9
2018, 55(9): 1853-1870.
DOI: 10.7544/issn1000-1239.2018.20180127
Abstract：
Blockchain technology is a new emerging technology that has the potential to revolutionize many traditional industries. Since the creation of Bitcoin, which represents blockchain 1.0, blockchain technology has been attracting extensive attention and a great amount of user transaction data has been accumulated. Furthermore, the birth of Ethereum, which represents blockchain 2.0, further enriches data type in blockchain. While the popularity of blockchain technology bringing about a lot of technical innovation, it also leads to many new problems, such as user privacy disclosure and illegal financial activities. However, the public accessible of blockchain data provides unprecedented opportunity for researchers to understand and resolve these problems through blockchain data analysis. Thus, it is of great significance to summarize the existing research problems, the results obtained, the possible research trends, and the challenges faced in blockchain data analysis. To this end, a comprehensive review and summary of the progress of blockchain data analysis is presented. The review begins by introducing the architecture and key techniques of blockchain technology and providing the main data types in blockchain with the corresponding analysis methods. Then, the current research progress in blockchain data analysis is summarized in seven research problems, which includes entity recognition, privacy disclosure risk analysis, network portrait, network visualization, market effect analysis, transaction pattern recognition, illegal behavior detection and analysis. Finally, the directions, prospects and challenges for future research are explored based on the shortcomings of current research.
Blockchain technology is a new emerging technology that has the potential to revolutionize many traditional industries. Since the creation of Bitcoin, which represents blockchain 1.0, blockchain technology has been attracting extensive attention and a great amount of user transaction data has been accumulated. Furthermore, the birth of Ethereum, which represents blockchain 2.0, further enriches data type in blockchain. While the popularity of blockchain technology bringing about a lot of technical innovation, it also leads to many new problems, such as user privacy disclosure and illegal financial activities. However, the public accessible of blockchain data provides unprecedented opportunity for researchers to understand and resolve these problems through blockchain data analysis. Thus, it is of great significance to summarize the existing research problems, the results obtained, the possible research trends, and the challenges faced in blockchain data analysis. To this end, a comprehensive review and summary of the progress of blockchain data analysis is presented. The review begins by introducing the architecture and key techniques of blockchain technology and providing the main data types in blockchain with the corresponding analysis methods. Then, the current research progress in blockchain data analysis is summarized in seven research problems, which includes entity recognition, privacy disclosure risk analysis, network portrait, network visualization, market effect analysis, transaction pattern recognition, illegal behavior detection and analysis. Finally, the directions, prospects and challenges for future research are explored based on the shortcomings of current research.
10
2018, 55(12): 2587-2599.
DOI: 10.7544/issn1000-1239.2018.20180623
Abstract：
With the advent of the medical big data era, knowledge interconnection has received extensive attention. How to extract useful medical knowledge from massive data is the key for medical big data analysis. Knowledge graph technology provides a means to extract structured knowledge from massive texts and images.The combination of knowledge graph, big data technology and deep learning technology is becoming the core driving force for the development of artificial intelligence. The knowledge graph technology has a broad application prospect in the medical domain. The application of knowledge graph technology in the medical domain will play an important role in solving the contradiction between the supply of high-quality medical resources and the continuous increase of demand for medical services.At present, the research on medical knowledge graph is still in the exploratory stage. The existing knowledge graph technology generally has several problems such as low efficiency, multiple restrictions and poor expansion in the medical domain. This paper firstly analyzes the medical knowledge graph architecture and construction technology for the strong professionalism and complex structure of big data in the medical domain. Secondly, the key technologies and research progress of the three modules of knowledge extraction, knowledge expression, knowledge fusion and knowledge reasoning in medical knowledge map are summarized. In addition, the application status of medical knowledge maps in clinical decision support, medical intelligence semantic retrieval, medical question answering system and other medical services are introduced. Finally, the existing problems and challenges of current research are discussed and analyzed, and its development is prospected.
With the advent of the medical big data era, knowledge interconnection has received extensive attention. How to extract useful medical knowledge from massive data is the key for medical big data analysis. Knowledge graph technology provides a means to extract structured knowledge from massive texts and images.The combination of knowledge graph, big data technology and deep learning technology is becoming the core driving force for the development of artificial intelligence. The knowledge graph technology has a broad application prospect in the medical domain. The application of knowledge graph technology in the medical domain will play an important role in solving the contradiction between the supply of high-quality medical resources and the continuous increase of demand for medical services.At present, the research on medical knowledge graph is still in the exploratory stage. The existing knowledge graph technology generally has several problems such as low efficiency, multiple restrictions and poor expansion in the medical domain. This paper firstly analyzes the medical knowledge graph architecture and construction technology for the strong professionalism and complex structure of big data in the medical domain. Secondly, the key technologies and research progress of the three modules of knowledge extraction, knowledge expression, knowledge fusion and knowledge reasoning in medical knowledge map are summarized. In addition, the application status of medical knowledge maps in clinical decision support, medical intelligence semantic retrieval, medical question answering system and other medical services are introduced. Finally, the existing problems and challenges of current research are discussed and analyzed, and its development is prospected.
11
2018, 55(11): 2452-2466.
DOI: 10.7544/issn1000-1239.2018.20170658
Abstract：
With the flourishing development of blockchain technology represented by bitcoin, the blockchain technology has moved from the era of programmable currency into the era of smart contract. The smart contract is an event-driven, state-based code contract and algorithm contract, which has been widely concerned and studied with the deep development of blockchain technology. The protocol and user interface are applied to complete all steps of the smart contract process. Smart contract enables users to implement personalized logic on the blockchain. The blockchain-based smart contract technology has the characteristics of de-centralization, autonomy, observability, verifiability and information sharing. It can also be effectively applied to build programmable finance and programmable society, which has been widely used in digital payment, financial asset disposal, multi-signature contract, cloud computing, Internet of things, sharing economy and other fields. The survey describes the basic concepts of smart contract technology, its whole life cycle, basic classification and structure, key technology, the art of the state, as well as its application scenarios and the main technology platforms. Its problems encountered at present are also discussed. Finally, based on the theoretical knowledge of the smart contract, we set up the Ethereum experimental environment and develop a system of crowdsale contract. The survey is aimed at providing helpful guidance and reference for future research of smart contract based on blockchain technology.
With the flourishing development of blockchain technology represented by bitcoin, the blockchain technology has moved from the era of programmable currency into the era of smart contract. The smart contract is an event-driven, state-based code contract and algorithm contract, which has been widely concerned and studied with the deep development of blockchain technology. The protocol and user interface are applied to complete all steps of the smart contract process. Smart contract enables users to implement personalized logic on the blockchain. The blockchain-based smart contract technology has the characteristics of de-centralization, autonomy, observability, verifiability and information sharing. It can also be effectively applied to build programmable finance and programmable society, which has been widely used in digital payment, financial asset disposal, multi-signature contract, cloud computing, Internet of things, sharing economy and other fields. The survey describes the basic concepts of smart contract technology, its whole life cycle, basic classification and structure, key technology, the art of the state, as well as its application scenarios and the main technology platforms. Its problems encountered at present are also discussed. Finally, based on the theoretical knowledge of the smart contract, we set up the Ethereum experimental environment and develop a system of crowdsale contract. The survey is aimed at providing helpful guidance and reference for future research of smart contract based on blockchain technology.
12
2016, 53(1): 165-192.
DOI: 10.7544/issn1000-1239.2016.20150661
Abstract：
Entity alignment on knowledge base has been a hot research topic in recent years. The goal is to link multiple knowledge bases effectively and create a large-scale and unified knowledge base from the top-level to enrich the knowledge base, which can be used to help machines to understand the data and build more intelligent applications. However, there are still many research challenges on data quality and scalability, especially in the background of big data. In this paper, we present a survey on the techniques and algorithms of entity alignment on knowledge base in decade, and expect to provide alternative options for further research by classifying and summarizing the existing methods. Firstly, the entity alignment problem is formally defined. Secondly, the overall architecture is summarized and the research progress is reviewed in detail from algorithms, feature matching and indexing aspects. The entity alignment algorithms are the key points to solve this problem, and can be divided into pair-wise methods and collective methods. The most commonly used collective entity alignment algorithms are discussed in detail from local and global aspects. Some important experimental and real world data sets are introduced as well. Finally, open research issues are discussed and possible future research directions are prospected.
Entity alignment on knowledge base has been a hot research topic in recent years. The goal is to link multiple knowledge bases effectively and create a large-scale and unified knowledge base from the top-level to enrich the knowledge base, which can be used to help machines to understand the data and build more intelligent applications. However, there are still many research challenges on data quality and scalability, especially in the background of big data. In this paper, we present a survey on the techniques and algorithms of entity alignment on knowledge base in decade, and expect to provide alternative options for further research by classifying and summarizing the existing methods. Firstly, the entity alignment problem is formally defined. Secondly, the overall architecture is summarized and the research progress is reviewed in detail from algorithms, feature matching and indexing aspects. The entity alignment algorithms are the key points to solve this problem, and can be divided into pair-wise methods and collective methods. The most commonly used collective entity alignment algorithms are discussed in detail from local and global aspects. Some important experimental and real world data sets are introduced as well. Finally, open research issues are discussed and possible future research directions are prospected.
13
2020, 57(2): 346-362.
DOI: 10.7544/issn1000-1239.2020.20190455
Abstract：
Large-scale data collection has vastly improved the performance of machine learning, and achieved a win-win situation for both economic and social benefits, while personal privacy preservation is facing new and greater risks and crises. In this paper, we summarize the privacy issues in machine learning and the existing work on privacy-preserving machine learning. We respectively discuss two settings of the model training process—centralized learning and federated learning. The former needs to collect all the user data before training. Although this setting is easy to deploy, it still exists enormous privacy and security hidden troubles. The latter achieves that massive devices can collaborate to train a global model while keeping their data in local. As it is currently in the early stage of the study, it also has many problems to be solved. The existing work on privacy-preserving techniques can be concluded into two main clues—the encryption method including homomorphic encryption and secure multi-party computing and the perturbation method represented by differential privacy, each having its advantages and disadvantages. In this paper, we first focus on the design of differentially-private machine learning algorithm, especially under centralized setting, and discuss the differences between traditional machine learning models and deep learning models. Then, we summarize the problems existing in the current federated learning study. Finally, we propose the main challenges in the future work and point out the connection among privacy protection, model interpretation and data transparency.
Large-scale data collection has vastly improved the performance of machine learning, and achieved a win-win situation for both economic and social benefits, while personal privacy preservation is facing new and greater risks and crises. In this paper, we summarize the privacy issues in machine learning and the existing work on privacy-preserving machine learning. We respectively discuss two settings of the model training process—centralized learning and federated learning. The former needs to collect all the user data before training. Although this setting is easy to deploy, it still exists enormous privacy and security hidden troubles. The latter achieves that massive devices can collaborate to train a global model while keeping their data in local. As it is currently in the early stage of the study, it also has many problems to be solved. The existing work on privacy-preserving techniques can be concluded into two main clues—the encryption method including homomorphic encryption and secure multi-party computing and the perturbation method represented by differential privacy, each having its advantages and disadvantages. In this paper, we first focus on the design of differentially-private machine learning algorithm, especially under centralized setting, and discuss the differences between traditional machine learning models and deep learning models. Then, we summarize the problems existing in the current federated learning study. Finally, we propose the main challenges in the future work and point out the connection among privacy protection, model interpretation and data transparency.
14
2009, 46(6): 1009-1018.
Abstract：
3D face recognition has become one of the most active research topics in face recognition due to its robustness in the variation on pose and illumination. 3D database is the basis of this work. Design and construction of the face database mainly include acquisition of prototypical 3D face data, preprocessing and standardizing of the data and the structure design. Currently, BJUT-3D database is the largest Chinese 3D face database in the world. It contains 1200 Chinese 3D face images and provides both the texture and shape information of human faces. This data resource plays an important role in 3D face recognition and face model. In this paper, the data description, data collection schema and the post-processing methods are provided to help using the data and future extension. A 3D face data dense correspondence method is introduced. Dense correspondence means that the key facials points are carefully labeled and aligned among different faces, which can be used for a broad range of face analysis tasks. As an application, a pose estimation and face recognition algorithm across different poses is proposed. Eexpremental results show that the proposed algorithm has a good performance.
3D face recognition has become one of the most active research topics in face recognition due to its robustness in the variation on pose and illumination. 3D database is the basis of this work. Design and construction of the face database mainly include acquisition of prototypical 3D face data, preprocessing and standardizing of the data and the structure design. Currently, BJUT-3D database is the largest Chinese 3D face database in the world. It contains 1200 Chinese 3D face images and provides both the texture and shape information of human faces. This data resource plays an important role in 3D face recognition and face model. In this paper, the data description, data collection schema and the post-processing methods are provided to help using the data and future extension. A 3D face data dense correspondence method is introduced. Dense correspondence means that the key facials points are carefully labeled and aligned among different faces, which can be used for a broad range of face analysis tasks. As an application, a pose estimation and face recognition algorithm across different poses is proposed. Eexpremental results show that the proposed algorithm has a good performance.
15
2018, 55(2): 327-337.
DOI: 10.7544/issn1000-1239.2018.20170228
Abstract：
With the trend of Internet of Everything, the number of end devices (e.g. smartphones, smart glasses) has increased rapidly which makes the data produced by these devices have grown at rates far more than the growth rate of network bandwidth. At the same time, the emergence of novel applications demands lower latency of the network, such as augmented reality and manless driving. Edge computing integrates any computing, storage and network resources at the edge of the network into a unified platform that provides services for users. This new computing model gets around the bottleneck that is caused by network bandwidth and latency, has received widespread attention in both industrial and academic. In this survey, we first introduce the concepts of edge computing and provide the definition of it. To help the readers to better understand the characteristics of edge computing, we compare it with cloud computing. We then analyze the three representative instances of edge computing platform and make a systematic comparison of them. After that, we enumerate some typical applications based on the edge computing to describe the advantages of edge computing in mobile or Internet of Things applications. Finally, the paper lays out several grand challenges of edge computing.
With the trend of Internet of Everything, the number of end devices (e.g. smartphones, smart glasses) has increased rapidly which makes the data produced by these devices have grown at rates far more than the growth rate of network bandwidth. At the same time, the emergence of novel applications demands lower latency of the network, such as augmented reality and manless driving. Edge computing integrates any computing, storage and network resources at the edge of the network into a unified platform that provides services for users. This new computing model gets around the bottleneck that is caused by network bandwidth and latency, has received widespread attention in both industrial and academic. In this survey, we first introduce the concepts of edge computing and provide the definition of it. To help the readers to better understand the characteristics of edge computing, we compare it with cloud computing. We then analyze the three representative instances of edge computing platform and make a systematic comparison of them. After that, we enumerate some typical applications based on the edge computing to describe the advantages of edge computing in mobile or Internet of Things applications. Finally, the paper lays out several grand challenges of edge computing.
16
2022, 59(1): 47-80.
DOI: 10.7544/issn1000-1239.20201055
Abstract：
In recent years, the application of deep learning related to graph structure data has attracted more and more attention. The emergence of graph neural network has made major breakthroughs in the above tasks, such as social networking, natural language processing, computer vision, even life sciences and other fields. The graph neural network can treat the actual problem as the connection between nodes in the graph and the message propagation problem, and the dependence between nodes can be modeled, so that the graph structure data can be handled well. In view of this, the graph neural network model and its application are systematically reviewed. Firstly, the graph convolutional neural network is explained from three aspects: spectral domain, spatial domain and pooling. Then, the graph neural network model based on the attention mechanism and autoencoder is described, and some graph neural network implemented by other methods are supplemented. Secondly, it summarizes the discussion and analysis on whether the graph neural network can be bigger and deeper. Furthermore, four frameworks of graph neural network are summarized. It also explains in detail the application of graph neural network in natural language processing and computer vision, etc. Finally, the future research of graph neural network is prospected and summarized. Compared with existing review articles on graph neural network, it elaborates the knowledge of spectral theory in detail, and comprehensively summarizes the development history of graph convolutional neural network based on the spectral domain. At the same time, a new classification standard, an improved model for the low efficiency of the spatial domain graph convolutional neural network, is given. And for the first time, it summarizes the discussion and analysis of graph neural network expression ability, theoretical guarantee, etc., and adds a new framework model. In the application part, the latest application of graph neural network is explained.
In recent years, the application of deep learning related to graph structure data has attracted more and more attention. The emergence of graph neural network has made major breakthroughs in the above tasks, such as social networking, natural language processing, computer vision, even life sciences and other fields. The graph neural network can treat the actual problem as the connection between nodes in the graph and the message propagation problem, and the dependence between nodes can be modeled, so that the graph structure data can be handled well. In view of this, the graph neural network model and its application are systematically reviewed. Firstly, the graph convolutional neural network is explained from three aspects: spectral domain, spatial domain and pooling. Then, the graph neural network model based on the attention mechanism and autoencoder is described, and some graph neural network implemented by other methods are supplemented. Secondly, it summarizes the discussion and analysis on whether the graph neural network can be bigger and deeper. Furthermore, four frameworks of graph neural network are summarized. It also explains in detail the application of graph neural network in natural language processing and computer vision, etc. Finally, the future research of graph neural network is prospected and summarized. Compared with existing review articles on graph neural network, it elaborates the knowledge of spectral theory in detail, and comprehensively summarizes the development history of graph convolutional neural network based on the spectral domain. At the same time, a new classification standard, an improved model for the low efficiency of the spatial domain graph convolutional neural network, is given. And for the first time, it summarizes the discussion and analysis of graph neural network expression ability, theoretical guarantee, etc., and adds a new framework model. In the application part, the latest application of graph neural network is explained.
17
2019, 56(1): 209-224.
DOI: 10.7544/issn1000-1239.2019.20180758
Abstract：
At present the smart education pattern supported by information technology such as big data analytics and artificial intelligence has become the trend of the development of education informatization, and also has become a popular research direction in academic hotspots. Firstly, we investigate and analyze the data mining technologies of two kinds of educational big data including teaching behavior and massive knowledge resources. Secondly, we focus on four vital technologies in teaching process such as learning guidance, recommendation, Q&A and evaluation, including learning path generation and navigation, learner profiling and personalized recommendations, online smart Q&A and precise evaluation. Then we compare and analyze the mainstream smart education platforms at home and abroad. Finally, we discuss the limitations of current smart education research and summarize the research and development directions of online smart learning assistants, learner smart assessment, networked group cognition, causality discovery and other smart education aspects.
At present the smart education pattern supported by information technology such as big data analytics and artificial intelligence has become the trend of the development of education informatization, and also has become a popular research direction in academic hotspots. Firstly, we investigate and analyze the data mining technologies of two kinds of educational big data including teaching behavior and massive knowledge resources. Secondly, we focus on four vital technologies in teaching process such as learning guidance, recommendation, Q&A and evaluation, including learning path generation and navigation, learner profiling and personalized recommendations, online smart Q&A and precise evaluation. Then we compare and analyze the mainstream smart education platforms at home and abroad. Finally, we discuss the limitations of current smart education research and summarize the research and development directions of online smart learning assistants, learner smart assessment, networked group cognition, causality discovery and other smart education aspects.
18
2017, 54(10): 2130-2143.
DOI: 10.7544/issn1000-1239.2017.20170470
Abstract：
With the development of smart home, intelligent care and smart car, the application fields of IoT are becoming more and more widespread, and its security and privacy receive more attention by researchers. Currently, the related research on the security of the IoT is still in its initial stage, and most of the research results cannot solve the major security problem in the development of the IoT well. In this paper, we firstly introduce the three-layer logic architecture of the IoT, and outline the security problems and research priorities of each level. Then we discuss the security issues such as privacy preserving and intrusion detection, which need special attention in the IoT main application scenarios (smart home, intelligent healthcare, car networking, smart grid, and other industrial infrastructure). Though synthesizing and analyzing the deficiency of existing research and the causes of security problem, we point out five major technical challenges in IoT security. They are privacy protection in data sharing, the equipment security protection under limited resources, more effective intrusion detection and defense systems and method, access control of equipment automation operations and cross-domain authentication of motive device. We finally detail every technical challenge and point out the IoT security research hotspots in future.
With the development of smart home, intelligent care and smart car, the application fields of IoT are becoming more and more widespread, and its security and privacy receive more attention by researchers. Currently, the related research on the security of the IoT is still in its initial stage, and most of the research results cannot solve the major security problem in the development of the IoT well. In this paper, we firstly introduce the three-layer logic architecture of the IoT, and outline the security problems and research priorities of each level. Then we discuss the security issues such as privacy preserving and intrusion detection, which need special attention in the IoT main application scenarios (smart home, intelligent healthcare, car networking, smart grid, and other industrial infrastructure). Though synthesizing and analyzing the deficiency of existing research and the causes of security problem, we point out five major technical challenges in IoT security. They are privacy protection in data sharing, the equipment security protection under limited resources, more effective intrusion detection and defense systems and method, access control of equipment automation operations and cross-domain authentication of motive device. We finally detail every technical challenge and point out the IoT security research hotspots in future.
19
2017, 54(5): 907-924.
DOI: 10.7544/issn1000-1239.2017.20160941
Abstract：
With the proliferation of Internet of things (IoT) and the burgeoning of 4G/5G network, we have seen the dawning of the IoE (Internet of everything) era, where there will be a huge volume of data generated by things that are immersed in our daily life, and hundreds of applications will be deployed at the edge to consume these data. Cloud computing as the de facto centralized big data processing platform is not efficient enough to support these applications emerging in IoE era, i.e., 1) the computing capacity available in the centralized cloud cannot keep up with the explosive growing computational needs of massive data generated at the edge of the network; 2) longer user-perceived latency caused by the data movement between the edge and the cloud;3) privacy and security concerns from data owners in the edge; 4) energy constraints of edge devices. These issues in the centralized big data processing era have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Leveraging the power of cloud computing, edge computing has the potential to address the limitation of computing capability, the concerns of response time requirement, bandwidth cost saving, data safety and privacy, as well as battery life constraint. “Edge” in edge computing is defined as any computing and network resources along the path between data sources and cloud data centers. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
With the proliferation of Internet of things (IoT) and the burgeoning of 4G/5G network, we have seen the dawning of the IoE (Internet of everything) era, where there will be a huge volume of data generated by things that are immersed in our daily life, and hundreds of applications will be deployed at the edge to consume these data. Cloud computing as the de facto centralized big data processing platform is not efficient enough to support these applications emerging in IoE era, i.e., 1) the computing capacity available in the centralized cloud cannot keep up with the explosive growing computational needs of massive data generated at the edge of the network; 2) longer user-perceived latency caused by the data movement between the edge and the cloud;3) privacy and security concerns from data owners in the edge; 4) energy constraints of edge devices. These issues in the centralized big data processing era have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Leveraging the power of cloud computing, edge computing has the potential to address the limitation of computing capability, the concerns of response time requirement, bandwidth cost saving, data safety and privacy, as well as battery life constraint. “Edge” in edge computing is defined as any computing and network resources along the path between data sources and cloud data centers. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction.
20
2015, 52(1): 229-247.
DOI: 10.7544/issn1000-1239.2015.20131340
Abstract：
With the development of information technology, emerging services based on Web2.0 technologies such as blog, microblog, social networks, and the Internet of things produce various types of data at an unprecedented rate, while cloud computing provides a basic storage infrastructure for big data. All of these lead to the arrival of the big data era. Big data contains great value. Data become the most valuable wealth of the enterprise, but big data also brings grand challenges. Personal privacy protection is one of the major challenges of big data. People on the Internet leave many data footprint with cumulativity and relevance. Personal privacy information can be found by gathering data footprint in together. Malicious people use this information for fraud. It brings many trouble or economic loss to personal life. Therefore, the issue of personal privacy has caused extensive concern of the industry and academia. However, there is little work on the protection of personal privacy at present. Firstly, the basic concepts of big data privacy protection are introduced, and the challenges and research on personal privacy concern are discussed. Secondly, the related technology of privacy protection is described from the data layer, application layer and data display layer. Thirdly, several important aspects of the personal privacy laws and industry standards are probed in the era of big data. Finally, the further research direction of personal privacy protection is put forward.
With the development of information technology, emerging services based on Web2.0 technologies such as blog, microblog, social networks, and the Internet of things produce various types of data at an unprecedented rate, while cloud computing provides a basic storage infrastructure for big data. All of these lead to the arrival of the big data era. Big data contains great value. Data become the most valuable wealth of the enterprise, but big data also brings grand challenges. Personal privacy protection is one of the major challenges of big data. People on the Internet leave many data footprint with cumulativity and relevance. Personal privacy information can be found by gathering data footprint in together. Malicious people use this information for fraud. It brings many trouble or economic loss to personal life. Therefore, the issue of personal privacy has caused extensive concern of the industry and academia. However, there is little work on the protection of personal privacy at present. Firstly, the basic concepts of big data privacy protection are introduced, and the challenges and research on personal privacy concern are discussed. Secondly, the related technology of privacy protection is described from the data layer, application layer and data display layer. Thirdly, several important aspects of the personal privacy laws and industry standards are probed in the era of big data. Finally, the further research direction of personal privacy protection is put forward.
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