云计算能够根据需求高效地提供计算服务。近年来，随着云计算的快速发展，数据规模急剧增长，在现有的计算标准中，云计算具有适应性设置、易于访问和以网络为中心的特点，这使得保护数据的隐私和安全性以及验证合规性变得困难。在将数据存储到不可信的云服务器之前，必须执行一系列的步骤来保证数据的安全性。在云的体系结构中，通过使用可访问的资源来分配任务，任务以这样的方式被调度到虚拟机中，以尽可能地减少执行时间。任务调度是一个NP-hard问题，其中定位资源的时间取决于问题的规模。该论文围绕在云计算中如何高效而安全的进行任务调度，引入了区块链机制对其进行研究，旨在利用区块链的优势来实现云环境中的效率和安全性。主要做了三个方面的工作：

⑴ 针对引入区块链机制时新区块产生时间长及效率低的问题，提出一种将文件块数据保存在相邻的哈希节点中的方法，从而得到最优区块大小，有效减少在区块链安排中产生新区块的时间，同时可提高交易安全等级。区块形成时间的中断和延迟被视作一个独立的原则进行测量，重点关注交易发生的时间以及交易之间安全的维护。为了解决的尺寸和传输时间的矛盾，文件块数据被保存在相邻的哈希节点中，调节两个目标任务，以获得一个最优的块大小，同时最小化时间。实验结果表明，在交易时间较少的情况下，交易的安全等级得到了提高。

⑵ 针对传统的粒子群算法易陷入局部最优的问题，提出一种基于改进粒子群优化算法（IPSO）和区块链的新型区块形成方法。将区块链的事务性原理引入传统的粒子群优化算法，从而使得改进后的粒子群优化算法在执行任务调度问题时，不但可以较快得到最优解，而且能保证安全的交易。首先，有效地遍历解的空间，当种群数量和迭代次数越高，IPSO达到最优解的可能性越高。其次，提出了一种基于区块链技术的新型区块形成技术，该技术将交易序列信息和并行机信息结合在一个交易节点上，并与所有交易节点相关联，形成交易列表。该方案进一步进行局部搜索。接着，通过一些有限制的迭代得到一个最优解。利用区块链的事务性原理，在调度中降低了makespan值和不平衡度，实现了机器之间的安全交易。最后，作者将该方法应用于作业车间任务调度的实际场景。仿真实验结果表明，所提出的算法可提高调度效率，实现安全交易，在调度中的作业车间问题的管理中是安全和有效的。

⑶ 针对云计算任务调度时出现的数据安全问题，提出一种基于区块链和神经网络的同态加密方法，使得数据驱动的加密成为可能，从而在保证安全性的同时，提高准确性和时间效率。该方法将全同态加密( FHE )、区块链和深度学习神经网络技术结合，并引入密码体制来保证数据安全交易。任务在每个云节点上传输之前，先应用了一种基于矩阵的同态加密算法- MORE对数据进行加密，并将其放入深度学习人工神经网络( ANN )模型中，这使得加密和解密可以直接在浮点数据上的神经网络中执行，从而有效地保持数据的隐私性和权威性。此外，区块链的使用提供了去中心化的身份验证，提高了数据精度，有助于增加操作功能。实验结果表明，本文提出的模型在准确率、运行时间和性能上都得到了提升，与之前的全同态技术相比，本文的方案更加有效。

With the rapid development of Cloud Computing, computational services are efficiently provided over the internet by Cloud Computing on demand. In Cloud Computing, people can distribute tasks by using accessible resources. Task scheduling is an NP-hard problem in which the time it takes to position the resources depends on the size of the problem. Cloud Computing is commonly characterized by the adaptable set-up, easy access, and net-centric technology, which makes it difficult to keep the privacy and security of data and validate compliance. A number of procedures must be implemented to assure the security of data before it is stored in an untrusted server of the cloud. Blockchain emerges as a promising technique for attaining clusters of cloud, providing data security, easy access, and storage.

       Therefore, we introduced the blockchain mechanism, to ensure safe and efficient task scheduling in cloud computing. First, we consider the formation of optimal blocks in task scheduling; second, since Particle Swarm Optimization (PSO) is a trusted algorithm utilized in existing operations, to schedule tasks using available resources in a cloud environment, we consider combining particle swarm optimization (PSO) and blockchain, an Improved Particle Swarm Optimization (IPSO) is proposed to improve scheduling efficiency and provide secure transactions; finally, we propose a blockchain and neural network-based homomorphic encryption method for cloud data security in task scheduling. The details are as follows:

       (1) With the introduction of a blockchain mechanism in order to reduce the time to generate a new block improving efficiency. We studied how to generate the optimal size of the block, for this reason, interruptions and delays in block formation time are measured as an independent principle, while focusing on the time of transactions and maintenance of security between the transactions. To solve this problem, the file block data is kept in adjacent hash nodes, and adjusting the two objective tasks to obtain an optimal block size while minimizing time. The results show that the security level of the transaction is improved when the transaction time is less.

        (2) The transactional principle of blockchain is introduced into the traditional particle swarm optimization algorithm PSO so that the improved particle swarm optimization algorithm (IPSO) can not only obtain the optimal solution quickly but also ensure safe transactions when performing task scheduling problems. First, in order to obtain the optimal solution by changing the dominant operation, the IPSO algorithm effectively traverses the space of the solution. The higher the population size and the number of iterations, the higher the possibility for the IPSO to achieve an optimal solution. The optimum solution remains unaffected to a specific level once the number of population and the number of iterations reaches that level.  Second, in blockchain, each block is revealed by a small sequence of random numbers formed by hash and is associated with a list of records. A novel block formation technique based on blockchain technology is proposed, by which transaction sequence information and parallel machine information are combined in a transaction node and associated with all transaction nodes to create a transaction list. This scheme makes further local searching. Afterward, it constantly explores the optimum solutions by carefully altering the selected machines of the leading transactions. Finally, an optimum solution is attained by means of some restricted iterations. Utilizing the transactional principle of blockchain, makespan value and degree of imbalance are reduced and secure transactions between machines are achieved in scheduling. To verify the execution performance of our improved algorithm IPSO, we apply the method to the job shop problem by implementing IPSO with a blockchain approach to provide efficient and secure job transactions for a set of operations. The performance of IPSO is assessed with reference to the makespan, execution time, improvement ratio, degree of imbalance, and security. Results of experiments specify that the proposed algorithm transcends the state-of-the-art task scheduling algorithms and is secure and efficient in the management of job shop problems in scheduling.

       (3) To solve the data security problems that arise during cloud computing task scheduling, we introduce a cryptographic system to ensure secure transactions of data. The efficacy of fully homomorphic encryption has constantly reflected its expediency. The efficacy of fully homomorphic encryption has always reflected its expediency. Nowadays, blockchain outsourcing can offer great applicability. Aiming at security issues, a combination of Fully Homomorphic Encryption (FHE), blockchain, and deep learning neural network technology is proposed. To form a secure and trusted data transmission mechanism, blockchain is used between different cloud nodes. To encrypt the data before being transmitted on each cloud node, a matrix-based homomorphic encryption algorithm—MORE is applied which allows the encryption and decryption to be executed directly within a neural network on floating-point data and put into the deep learning Artificial Neural Network (ANN) model, hence we efficiently keep the privacy and authority of data. Furthermore, the use of blockchain offers decentralized authentication which increases data precision and helps to increase operational functions. In a decentralized mode, blockchain with fully homomorphic encryption can help to secure important information without disclosing the data of the provider party, and allow the third parties to use the information in an encrypted form. The experimental results present that improved accuracy, runtime, and performance are attained with this proposed model. The proposed scheme is safe and more effective compared with the previous fully homomorphic techniques.

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