Global Governance in Cyberspace: Process,Conflict and China’s Proposals

In recent years,problems such as cyber warfares,cyber espionages and cyber crimes have become global public nuisances and always challenge the authority of the sovereign states. Therefore,the governance of the derivative problems concerning the global cyberspace has become an issue of importance in the international law. To overcome these challenges,sovereign states begin to get involved in cyberspace and seek international cooperation,so as to promote the evolution of the international rule of law in cyberspace. Currently,the cyberspace is filled with contradictions and conflicts. The cyber hegemonic states monopolize basic resources for cyberspaces,and their liberalism of double standards is the best tool to maintain their centralized and dominant situations. Moreover,the differences on governance problems between different camps leads to the significant institutional supply shortage. Accordingly,as a responsible power,China should advocate the Chinese solution and wisdom so as to create the reform of global cyberspace governance system. Possible specific measures include clarifying the governance structure of sovereign state-orientation,adhering to the model of shared governance with multilateral approach and multi-party participation.

A Pricing-Based Cooperative Relay Selection Scheme for Reliable Communications

With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and efficiency.However,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission efficiency.To enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction strategies.We propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration process.Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

Participants Recruitment for Coverage Maximization by Mobility Predicting in Mobile Crowd Sensing

Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.

The deep spatiotemporal network with dual-flow fusion for video-oriented facial expression recognition

The video-oriented facial expression recognition has always been an important issue in emotion perception.At present,the key challenge in most existing methods is how to effectively extract robust features to characterize facial appearance and geometry changes caused by facial motions.On this basis,the video in this paper is divided into multiple segments,each of which is simultaneously described by optical flow and facial landmark trajectory.To deeply delve the emotional information of these two representations,we propose a Deep Spatiotemporal Network with Dual-flow Fusion(defined as DSN-DF),which highlights the region and strength of expressions by spatiotemporal appearance features and the speed of change by spatiotemporal geometry features.Finally,experiments are implemented on CKþand MMI datasets to demonstrate the superiority of the proposed method.

A notary group-based cross-chain mechanism

As an emerging distributed technology, blockchain has begun to penetrate into many fields such as finance, healthcare, supply chain, intelligent transportation. However, the interoperability and value exchange between different independent blockchain systems is restricting the expansion of blockchain. In this paper, a notary group-based cross-chain interaction model is proposed to achieve the interoperability between different blockchains. Firstly, a notary election mechanism is proposed to choose one notary from the notary group to act as a bridge for cross-chain transactions. Secondly, a margin pool is introduced to limit the misconduct of the elected notary and ensure the value transfer between the involved blockchains. Moreover, a reputation based incentive mechanism is used to encourage members of the notary group to participate in cross-chain transactions. Ethereum-based experiments demonstrate that the proposed mechanism can provide an acceptable performance for cross-chain transactions and provide a higher security level than ordinary cross-chain mechanisms.

IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data

Vertical Federated Learning(VFL)has many applications in the field of smart healthcare with excellent performance.However,current VFL systems usually primarily focus on the privacy protection during model training,while the preparation of training data receives little attention.In real-world applications,like smart healthcare,the process of the training data preparation may involve some participant's intention which could be privacy information for this partici-pant.To protect the privacy of the model training intention,we describe the idea of Intention-Hiding Vertical Feder-ated Learning(IHVFL)and illustrate a framework to achieve this privacy-preserving goal.First,we construct two secure screening protocols to enhance the privacy protection in feature engineering.Second,we implement the work of sample alignment bases on a novel private set intersection protocol.Finally,we use the logistic regression algorithm to demonstrate the process of IHVFL.Experiments show that our model can perform better efficiency(less than 5min)and accuracy(97%)on Breast Cancer medical dataset while maintaining the intention-hiding goal.

Authentication scheme for industrial Internet of things based on DAG blockchain

Internet of things(IoT) can provide the function of product traceability for industrial systems. Emerging blockchain technology can solve the problem that the current industrial Internet of things(IIoT) system lacks unified product data sharing services. Blockchain technology based on the directed acyclic graph(DAG) structure is more suitable for high concurrency environments. But due to its distributed architecture foundation, direct storage of product data will cause authentication problems in data management. In response, IIoT based on DAG blockchain is proposed in this paper, which can provide efficient data management for product data stored on DAG blockchain, and an authentication scheme suitable for this structure is given. The security of the scheme is based on a discrete-logarithm-based assumption put forth by Lysyanskaya, Rivest, Sahai and Wolf(LRSW) who also show that it holds for generic groups. The sequential aggregation signature scheme is more secure and efficient, and the new scheme is safe in theory and it is more efficient in engineering.

Dynamic multi-keyword fuzzy ranked search with leakage resilience over encrypted cloud data

To achieve the confidentiality and retrievability of outsourced data simultaneously,a dynamic multi-keyword fuzzy ranked search scheme(DMFRS)with leakage resilience over encrypted cloud data based on two-level index structure was proposed.The first level index adopts inverted index and orthogonal list,combined with 2-gram and location-sensitive Hashing(LSH)to realize a fuzzy match.The second level index achieves user search permission decision and search result ranking by combining coordinate matching with term frequency-inverse document frequency(TF-IDF).A verification token is generated within the results to verify the search results,which prevents the potential malicious tampering by cloud service providers(CSP).The semantic security of DMFRS is proved by the defined leakage function,and the performance is evaluated based on simulation experiments.The analysis results demonstrate that DMFRS gains certain advantages in security and performance against similar schemes,and it meets the needs of storage and privacy-preserving for outsourcing sensitive data.