MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Mutation testing is devised for measuring test suite adequacy by identifying the artificially induced faults in software. This paper presents a novel approach by considering multiobjectives-based optimization. Here, the optimal test suite generation is performed using the proposed water cycle water wave optimization (WCWWO). The best test suites are generated by satisfying the multiobjective factors, such as time of execution, test suite size, mutant score, and mutant reduction rate. The WCWWO is devised by a combination of the water cycle algorithm (WCA) and water wave optimization (WWO). The hierarchical attention network (HAN) is used for classifying the equivalent mutants by utilizing the MutPy tool. Furthermore, the performance of the developed WCWWO+HAN is evaluated in terms of three metrics—mutant score (MS), mutant reduction rate (MRR), and fitness— with the maximal MS of 0.585, higher MRR of 0.397, and maximum fitness of 0.652.

School of Computer Engineering & Technology

Engineering and Technology

Fulltext

International Journal of Swarm Intelligence Research

Multi-Objective Optimization Model and Hierarchical Attention Networks for Mutation Testing

The data from the mobile devices and internet services plays a significant role now a day. The information shared needs to be original to get the best solution. Without the utilization of privacy preservation techniques, the individual's sensitive information cannot be exposed. The Privacy preserving data publishing (PPDP) is one of the privacy preserving techniques to preserve privacy. However, the reduction of the loss of data and the security enhancement are the major challenges. To solve the issues in secure data publishing, the efficient Cat Swarm Henry Gas Solubility Optimization (CSHGSO) is introduced for achieving effective privacy preservation for secure data publishing. In addition, the proposed CSHGSO is devised by the incorporation of Cat Swarm Optimization (CSO), and Henry Gas Solubility Optimization (HGSO). Here, the procedure of generating secret key is carried out based on the proposed CSHGSO with respect to the objective measures. The experimentation of the developed CSHGSO is performed in JAVA tool using Heart Disease Dataset. Furthermore, the CSHGSO achieved the higher conditional privacy of 1.338 using database-1, maximum accuracy of 0.965 using database-4, and the NMI value of 0.927 using database-4.

Advances in Engineering Software

Optimized key generation-based privacy preserving data mining model for secure data publishing

In the Wireless Sensor Network (WSN), the data prediction approach is needed to attain data effectively by diminishing node energy consumption. Hence, in this research, Water cycle Fire Fly Optimization and Deep Long Short-Term Memory (WCFO+ Deep LSTM) approach is employed for aggregation and reduction of data. The processes involved in the developed method are node simulation, cluster-based topology construction, routing tree construction, and data aggregation. Initially, IoT nodes are simulated in the network environment. The cluster-based topology construction is made using the WCFO algorithm. The WCFO is developed by the integration of the Firefly Optimization Algorithm (FOA) and Water cycle algorithm (WCA). The cluster-based topology is constructed by considering the objective function that includes the parameters, including distance, delay, link quality, and energy. After that, the routing process is performed using developed WCFO approach for constructing a routing tree and estimating the optimal path. Finally, the Deep LSTM is trained by the proposed WCFO algorithm, which is utilized for executing data reduction and data aggregation process with minimum energy consumption. The devised WCFO+ Deep LSTM approach achieved better performance in terms of prediction error, delay, energy, and Packet delivery ratio (PDR) with values 0.029, 0.001s, 0.161J and 99.054%, respectively. © 2023 World Scientific Publishing Company.

Journal of Uncertain Systems

Optimal Cluster-Based Topology and Deep LSTM-Based Prediction Method for Data Reduction in IoT

Wireless Sensor Network (WSN) achieved great attention in different domains due to the easy maintenance, scalability characteristics, and self-configuration. It is arranged with small-sized sensors that are interacted with the Internet of Things (IoT) to observe and records physical environmental conditions. Due to the dynamic nature and less resources of sensor nodes, searching for the optimal and secure routing path results in a challenging task. Establishing a secure communication path between the source and the destination node is more significant in the network scenario to accomplish the process of data routing. Accordingly, the failure of nodes along with the routing path result in path failure and it results in data loss. Therefore, an effective data routing mechanism is developed in this research to ensure the communication process to be more effective by employing the proposed Whale Spotted Hyena Optimization (WSHO) algorithm. The proposed WSHO is derived by the incorporation of the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimizer (SHO), respectively. Accordingly, the process of data routing is accomplished by considering factors, like energy dissipation, distance, trust, and delay. The data security process is employed to secure the communicated data through the privacy preservation model with different security factors. Moreover, the proposed method achieved higher performance in terms of energy and trust 0.4432J, and 0.5124, respectively.

Ad Hoc & Sensor Wireless Networks

WSHO: Energy and Trust Aware Protocol Using Whale Spotted Hyena Optimization Algorithm in IoT Integrated WSN

This paper introduces an algorithm, termed self-adaptive genetic grey wolf optimizer (self-adaptive genetic GWO), for privacy preservation using a C-mixture factor. The C-mixture factor improves the privacy of data, in which the data does not satisfy the privacy constraints, such as l-diversity, m-privacy and k-anonymity. Experimentation is carried out using the adult dataset and the effectiveness of the proposed self-adaptive genetic GWO is checked depending on the information loss and the average equivalence class metric values and is evaluated to be the best when compared to other existing techniques with low information loss value as 0.1724 and average equivalence class value as 0.71, respectively.

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Advances in Intelligent Systems and Computing

An approach for collaborative data publishing using self-adaptive genetic grey wolf optimizer

The security of privacy is currently a field of critical importance in data mining. Organizations and corporations in this dynamic environment are constantly trying to somehow get the database of their competitors. A detailed review of such databases can be done to retrieve a variety of confidential and sensitive information, links, connections, inferences, and findings. It can implicitly or explicitly cause a major loss to the database owner. The owners of the database sell their data to third parties for money. If a database is not held until it is revealed to a third party, then the data owner can suffer disasters. We consider the issue of collective publication of data to anonymize horizontally separated data on multiple data providers. The proposed work discusses and contributes to this new challenge of data publishing. First, we introduce the notion of group-based classification, which guarantees that the anonymized data satisfies a given privacy constraint against any group of data providers. We present a collaborative data publishing model with privacy preservation for efficiently checking in a group of records. Privacy preservation can be used to identify abnormal behavior in data sharing. Many privacy-preserving data publishing techniques were developed but failed to consider the data set because of its complexity and domain specific nature. Various organizations release information about persons in public for resource sharing. But maintaining the confidentiality of individual information is a difficult task with various data releases from multiple organizations where coordinating before data publication. The proposed model is compared with traditional models and the results depicts that proposed model exhibits better performance. © 2022 Scrivener Publishing LLC.

Machine Learning Paradigm for Internet of Things Applications

A collaborative data publishing model with privacy preservation using group-based classification and anonymity

Due to increasing need of using distributed databases, high demand presents on sharing data to easily update and access the useful information without any interruption. The sharing of distributed databases causes a serious issue of securing information since the databases consist of sensitive personal information. To preserve the sensitive information and at the same time, releasing the useful information, a significant effort is made by the researchers under privacy preserving data publishing that have been receiving considerable attention in recent years. In this work, a new privacy measure, called c-mixture is introduced to maintain the privacy constraint without affecting utility of the database. In order to apply the proposed privacy measure to privacy preserving data publishing, a new algorithm called, CPGEN is developed using genetic algorithm and multi-objective constraints. The proposed multi-objective optimization considered the multiple privacy constraints along with the utility measurement to measure the importance. Also, the proposed CPGEN is adapted to handle the cold-start problem which commonly happened in distributed databases. The proposed algorithm is experimented with adult dataset and quantitative performance is analyzed using generalized information loss and average equivalence class size metric. From the experimentation, we proved that the proposed algorithm maintained the privacy and utility as compared with the existing algorithm.

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”C-mixture and multi-constraints based genetic algorithm forcollaborative data publishing

Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts. The concept of data publishing faces a lot of security issues, indicating that when any trusted organization provides data to a third party, personal information need not be disclosed. Therefore, to maintain the privacy of the data, this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer (Genetic GWO) algorithm for which a C-mixture parameter is used. The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints, such as the k-anonymity, l-diversity and the m-privacy. A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size. The minimum value of the fitness ensures the maximum utility and the maximum privacy. Experimentation was carried out using the adult dataset, and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9, respectively.