As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.

The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at the hands of internet miscreants. A phishing attack comprises an attacker that creates fake websites to fool users and steal client-sensitive data which may be in form of login, password, or credit card details. Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection. This thesis will discuss in detail, different approaches used by various authors over the past few years. This survey aims to identify and narrow down the best machine learning algorithms that can be adopted to develop a hybrid model which can be implemented to detect whether a website is legitimate or phishing in nature.

PURPOSE: With a focus on enhancing transparency, lowering the risk of fraud, and ensuring ethical sourcing practices, this research aims to investigate how blockchain and IoT technologies can be incorporated into the diamond supply chain. This study addresses the complexities and challenges of implementing these technologies in an industry characterized by fragmented information sharing and centralized data storage. DESIGN/METHODOLOGY/APPROACH: Using both qualitative and quantitative analysis, the research uses a mixed-methods approach. While secondary data was obtained from previously published works, industry reports, and case studies, primary data was gathered through semi-structured interviews with professionals in the field. The implementation of the prototype system was carried out in three phases: Define, Operate, and Test. Ethereum was chosen for its smart contract capabilities, and various IoT sensors were deployed to monitor environmental conditions and track the real-time location of diamonds. FINDINGS: The integration of blockchain and IoT technologies significantly enhanced transparency within the diamond supply chain. The immutable nature of blockchain ensured tamper-proof records of transactions, while IoT sensors provided continuous real-time data, reinforcing transparency. The study observed a notable reduction in fraud due to the robust mechanisms of the system, which detected and prevented unauthorized alterations to the recorded data. Smart contracts automated compliance checks, ensuring adherence to ethical standards. Quantitative analysis revealed improvements in key metrics such as fraud reduction rates, transparency enhancements, and adherence to ethical sourcing standards. ORIGINALITY/VALUE: This study bridges a notable gap in existing research by focusing on the diamond supply chain. It provides comprehensive, data-driven insights and practical recommendations for industry stakeholders and policymakers. The results highlight how combining blockchain and IoT technology can improve operational efficiency, transparency, and ethical practices in the diamond business. It is also feasible and scalable. The study's methods and findings add a great deal to the body of information already in existence and provide a framework for further investigation and application in related situations.

Heart disease has reached to the number one position in last decade in terms of mortality rate, and more wretchedly, heart attack has affected life in 80 of the cases. Cardiac arrest is an incurable incongruity that requires special treatment and cure. It has been a key research area for many years, and the number of researchers across the globe is devoted toward finding the optimal solution to avoid the ill-effect of this disease. Along with predicting heart disease, if focus moves towards prevention of heart attack as well, then this could result in major life saver area for masses. This research work is fully devoted toward finding out the probability of heart attack so that people can take preventive measure before it hit the wall. This research proposed the neural fuzzy inference system (NFIS) to represent the training data formed from the n-dimensions of functions. The NFIS consists of error computing module to improve the learning instructions when the errors have been measured, initially the membership functions are defined, and the parameters of membership functions are activated and learnt through when needed for an operation. The proposed methodology has experimented with sample test cases on Cleveland heart disease dataset from University of California Irvine (UCI) repository with the integration of supporting dependable and nondependable parameters, causing-factors, and data-matrices. This research has integration more than 13000 fuzzification rules to generate best decision-making, normalization process, planting techniques to create the feasibility to compute the heart attack probability and achieved 94 percentage of accuracy. This research can be extendable to build auto-altering and advise system with integration hardware peripheral circuit devices.