A Sharding Scheme-Based Many-Objective Optimization Algorithm for Enhancing Security in Blockchain-Enabled Industrial Internet of Things

Cai, Xingjuan; Geng, Shaojin; Zhang, Jingbo; Wu, Di; Zhang, Wensheng; Chen, Jinjun

doi:10.1109/tii.2021.3051607

Cited by 96 publications

(54 citation statements)

References 31 publications

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“…In order to highlight the advantages of the proposed algorithm, we compare DPPMOA with other algorithms in DTLZ (1-7) and WFG (1)(2)(3)(4)(5)(6)(7)(8)(9), and analyze their SP, IGD, and coverage. The results are shown in Tables 3-5.…”

Section: Results On the Dtlz And Wfg Benchmark Setmentioning

confidence: 99%

Adaptive differential privacy preserving based on multi‐objective optimization in deep neural networks

Fan

Cui

2021

Concurrency and Computation

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Privacy data security has become an important bottleneck for the overall development of artificial intelligence and a key challenge that needs to be broken in the Internet era. The current research mainly considers differential privacy to effectively protect the private information in the data. However, as the noise increases, the precision of the training model will decrease. In order to solve above problem, an adaptive differential privacy (ADP) method is constructed and applied to deep neural networks.ADP adds noise adaptively in the training process according to the importance of features. We also build the differential privacy multi-objective optimization model (DPMOM). DPMOM adopts multi-objective optimization characteristics, takes accuracy and privacy protection as the optimization objectives. It optimizes the super parameters of deep neural networks and the noise of differential privacy. In addition, to better solve the ADP model, with the NSGA-II algorithm as the basic framework, a multi-objective optimization algorithm based on differential privacy protection (DPP-MOA) is designed. Simulation experiments show that compared with other machine learning methods and differentially private stochastic gradient descent, the accuracy of ADP is higher under the same amount of noise. Through comparison with NSGA-II, IBEA, PESA-II, and AGE-II, DPPMOA is proved that the solution set of this algorithm is better. K E Y W O R D S deep neural networks, differential privacy protection, multi-objective optimization algorithm, multi-objective optimization model INTRODUCTIONPrivate information under the Internet has the characteristics of large amount of data, multiple categories, and complex hierarchical relationships. The existing privacy protection technologies based on anonymization and data encryption need to closely rely on background knowledge assumptions, 1 which can only ensure that privacy on a single data set is not leaked. The limitations are often difficult to meet the requirements for big data privacy protection under the Internet. Therefore, people began to pay attention to privacy protection technologies and their applications such as secure multi-party computing, homomorphic encryption, and differential privacy.K-anonymity 2 protects user privacy by anonymizing data. To prevent quasi-identifiers from forming correspondence with other sets containing key attributes and quasi-identifiers, the key attributes are deleted before the data table is published and the quasi-identifiers are anonymized, making each record indistinguishable from the other records. However, data are protected by deleting the identifier of the traditional data before the data table is released, and the data table is vulnerable to chain attacks, causing user privacy exposure. As an important research direction of cryptography, secure multi-party computation was first proposed by Yao 3 to solve the problem of a group of mutually distrustful participants each holding

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Section: Results On the Dtlz And Wfg Benchmark Setmentioning

confidence: 99%

Adaptive differential privacy preserving based on multi‐objective optimization in deep neural networks

Fan

Cui

2021

Concurrency and Computation

Self Cite

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“…Reliability and high requirements for real-time communications are very important in IIoT. The authors of [30] proposed a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP). It optimizes the shared validation validity model.…”

Section: Related Workmentioning

confidence: 99%

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

Rehman

Haseeb

Lloret

et al. 2021

Sensors

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In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.

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“…As an indicator for evaluating the entire system, the coverage is calculated directly after recommendation for all users in the system. Table 2 shows the comparison effect of the NSGA-III [24], RVEA [25], MaOEA-SIN [38], NSGAIII-GBFE [34], and MaPSOMC-RS on the F-measure, diversity, novelty, and coverage of the recommendation model. At the same time, since each algorithm will produce a set of solutions (providing users with multiple recommendation lists), each recommendation list has different performance in the 4 evaluation indicators.…”

Section: A Experimental Settingsmentioning

confidence: 99%

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System

2021

KSII TIIS

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Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE + are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

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A Sharding Scheme-Based Many-Objective Optimization Algorithm for Enhancing Security in Blockchain-Enabled Industrial Internet of Things

Cited by 96 publications

References 31 publications

Adaptive differential privacy preserving based on multi‐objective optimization in deep neural networks

Adaptive differential privacy preserving based on multi‐objective optimization in deep neural networks

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System

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