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.

Background: According to the renowned and Oscar award-winning American actor and film director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences caused by privacy and data breaches. The Indian healthcare industry is one of the largest and rapidly developing industry. Overall, healthcare management is changing from disease-centric into patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management, and the privacy of patient records must receive equal attention. Purpose: This paper mainly presents the utility and privacy factors of the Indian healthcare data and discusses the utility aspect and privacy problems concerning Indian healthcare systems. It defines policies that reform Indian healthcare systems. The case study of the NITI Aayog report is presented to explain how reformation occurs in Indian healthcare systems. Findings: It is found that there have been numerous research studies conducted on Indian healthcare data across all dimensions; however, privacy problems in healthcare, specifically in India, are caused by prevalent complacency, culture, politics, budget limitations, large population, and existing infrastructures. This paper reviews the Indian healthcare system and the applications that drive it. Additionally, the paper also maps that how privacy issues are happening in every healthcare sector in India. Originality/Value: To understand these factors and gain insights, understanding Indian healthcare systems first is crucial. To the best of our knowledge, we found no recent papers that thoroughly reviewed the Indian healthcare system and its privacy issues. The paper is original in terms of its overview of the healthcare system and privacy issues. Social Implications: Privacy has been the most ignored part of the Indian healthcare system. With India being a country with a population of 130 billion, much healthcare data are generated every day. The chances of data breaches and other privacy violations on such sensitive data cannot be avoided as they cause severe concerns for individuals. This paper segregates the healthcare system’s advances and lists the privacy that needs to be addressed first.

The purpose of this paper is to present the bibliometric study of articles IT Infrastructure Management to Avoid Failure Conditions. As in today’s era of IT Industries, IT infrastructure management plays a crucial role. As a result, substantial research is going on to improve the reliability and availability of assets in IT infrastructure. The paper analyzes and focuses the results acquired from articles accessed from Scopus for the past 15 years by examining in terms of frequently used keywords, the amount of work done in different countries and year-wise progression of the research, prolific authors, article citation frequencies, etc. Tools such as Gephi, Word Cloud, BiblioShiny, GPS visualizer, etc. are used for bibliometric analysis. The study comes out with maximum publications of IT infrastructure management from conferences and journals. Anomaly detection, log analysis and learning system are the most frequently used keywords in the publications. Significant research has been done in the USA, followed by China under the area of Computer Science with an increase in publication since 2018. This paper provides an accurate idea about the amount of work done in different countries and year-wise progression of the research. This bibliometric analysis will be useful for beginners to conduct a literature survey using appropriate literature available on the Scopus database.

There are tremendous users of social media networks, and they keep growing day by day. In recent years, social media networks have sparked widespread interest among the general public because they offer a simple and appealing form of communication. Users’ data is at risk due to their expanding contact with social networks, necessitating securing their privacy. Users communicate with each other on social media and share information that is vital and private. This user’s information is on the tip of attraction, as many third-party ethical users use it for good causes like increasing the new customers by analyzing their needs. Still, unethical users misuse it for destructive purposes like stealing data, burglary, or personification. And hence the data owner’s task is to preserve the privacy of information. Before publishing such information in the public domain, it needs to be anonymized so that a third party cannot misuse it. Again balancing privacy and utility also need to be focused. The proposed improved Mondrian algorithm anonymizes data by partitioning and enhances privacy and utility balance on the Adult dataset same as with measures PIRL, NCP and DP.