A Trust Value Sharing Scheme in Heterogeneous Identity Federation Topologies

Recent developments in heterogeneous identity federation systems have heightened the need for the related trust management system.The trust management system evaluates,manages,and shares users’trust values.The service provider(SP)members of the federation system rely on users’trust values to determine which type and quality of service will be provided to the users.While identity federation systems have the potential to help federated users save time and energy and improve service experience,the benefits also come with significant privacy risks.So far,there has been little discussion about the privacy protection of users in heterogeneous identity federation systems.In this paper,we propose a trust value sharing scheme based on a proxy ring signature for the trust management system in heterogeneous identity federation topologies.The ring signature schemes can ensure the validity of the data and hide the original signer,thereby protecting privacy.Moreover,no group manager participating in the ring signature,which naturally matches with our decentralized heterogeneous identity federation topologies.The proxy signature can reduce the workload of the private key owner.The proposed scheme shortens the calculation time for verifying the signature and then reduces the overall time consumption in the process of trust sharing.Our studies prove that the proposed scheme is privacy-preserving,efficient,and effective.

Trust model based on individual experience

To improve the accuracy of node trust evaluation in a distributed network, a trust model based on the experience of individuals is proposed, which establishes a new trust assessment system by introducing the experience factor and the comparative experience factor. The new evaluation system considers the differences between individuals and interactive histories between nodes, which solves the problem that nodes have inaccurate assessments due to the asymmetry of nodes to a certain extent. The algorithm analysis indicates that the new model uses different deviating values of tolerance evaluation for different individuals and uses different updating values embodying node individuation when updating feedback credibility of individuals, which evaluates the trust value more reasonably and more accurately. In addition, the proposed algorithm can be used in various trust models and has a good scalability.

DPTSV:A Dynamic Priority Task Scheduling Strategy for TSS Deadlock Based on Value Evaluation

This paper analyzes the threat of TCG Software Stack(TSS)/TCM Service Module(TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism.In addition,this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat.The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue.In this strategy,an algorithm for real-time value computing is also designed,and it can adjust the production curves of the real time value by setting parameters in different environment,thus enhancing its adaptability,which is followed by scheduling and algorithm description.This paper also implements the algorithm and carries out its performance optimization.Due to the experiment result from Intel NUC,it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.

DERNNet:Dual Encoding Recurrent Neural Network Based Secure Optimal Routing in WSN

A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical regions.The basic challenge experienced while designing WSN is in increasing the network lifetime and use of low energy.As sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in WSN.Nodes energy is considered as an important resource for sensor node which are battery powered based.In WSN,energy is consumed mainly while data is being transferred among nodes in the network.Several research works are carried out focusing on preserving energy of nodes in the network and made network to live longer.Moreover,this network is threatened by attacks like vampire attack where the network is loaded by fake traffic.Here,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the network.Moreover,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network nodes.The proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various parameters.These existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the network.Hence,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.

Consensus algorithm for medical data storage and sharing based on master–slave multi-chain of alliance chain

The safe storage and sharing of medical data have promoted the development of the public medical field.At the same time,blockchain technology guarantees the safe storage and sharing of medical data.However,the consensus algorithm in the current medical blockchain cannot meet the requirements of low delay and high throughput in the large-scale network,and the identity of the primary node is exposed and vulnerable to attack.Therefore,this paper proposes an efficient consensus algorithm for medical data storage and sharing based on a master–slave multi-chain of alliance chain(ECA_MDSS).Firstly,institutional nodes in the healthcare alliance chain are clustered according to geographical location and medical system structure to form a multi-zones network.The system adopts master–slave multi-chain architecture to ensure security,and each zone processes transactions in parallel to improve consensus efficiency.Secondly,the aggregation signature is used to improve the practical Byzantine fault-tolerant(PBFT)consensus to reduce the communication interaction of consensus in each zone.Finally,an efficient ring signature is used to ensure the anonymity and privacy of the primary node in each zone and to prevent adaptive attacks.Meanwhile,a trust model is introduced to evaluate the trust degree of the node to reduce the evil done by malicious nodes.The experimental results show that ECA_MDSS can effectively reduce communication overhead and consensus delay,improve transaction throughput,and enhance system scalability.