Online social networks (OSNs) have transformed how users share personal information, yet they also pose significant privacy risks due to the vast accumulation of sensitive data. Protecting user privacy is critical, as data exposure can lead to identity theft, unauthorized access, and misuse. While OSN data is valuable for businesses, governments, and researchers, its release in original form raises privacy concerns, as conventional anonymization techniques struggle to address the demographic richness of these networks. This paper presents a comprehensive review of privacy challenges and anonymization strategies in OSNs. We analyze state-of-the-art privacy-preserving mechanisms, explore common privacy breaches, and assess classical anonymization techniques along with their limitations. The review identifies specific privacy risks, such as data harvesting, cyberbullying, targeted profiling, and social engineering attacks. It also examines attacker methodologies leveraging background knowledge to breach privacy. Our key contributions include highlighting challenges in anonymizing OSN data, reviewing advancements in privacy preservation, and identifying open issues requiring further research.

Summary Cloud's operating‐system‐level virtualization has introduced a new phase of lightweight virtualization through containers. The architecture of cloud‐native and microservices‐based application development strongly advocates for the use of containers due to their swift and convenient deployment capabilities. However, the security of applications within containers is important, as malicious or vulnerable content could jeopardize the container and the host system. This vulnerability also extends to neighboring containers and may compromise data integrity and confidentiality. The article focuses on developing an intrusion detection system tailored to containerized cloud environments by identifying system call analysis techniques and also proposes an anomaly‐based host intrusion detection system (Ab‐HIDS). This system employs the frequency of N‐grams system calls as distinctive features. To enhance performance, two ensemble learning models, namely voting‐based ensemble learning and XGBoost ensemble learning, are employed for training and testing the data. The proposed system is evaluated using the Leipzig Intrusion Detection Data Set (LID‐DS), demonstrating substantial performance compared to existing state‐of‐the‐art methods. Ab‐HIDS is validated for class imbalance using the imbalance ratio and synthetic minority over‐sampling technique methods. Our system achieved significant improvements in detection accuracy with 4% increase for the voting‐based ensemble model and 6% increase for the XGBoost ensemble model. Additionally, we observed reductions in the false positive rate by 0.9% and 0.8% for these models, respectively, compared to existing state‐of‐the‐art methods. These results illustrate the potential of our proposed approach in improving security measures within containerized environments.

Summary Location‐based services (LBS) has become an intrinsic part of our everyday life. However, the flexibility and convenience provided by LBS are at the cost of user privacy since untrusted LBS server can leak private information of users. To overcome the privacy issues observed in LBS, a novel dummy‐generation based privacy preservation technique is proposed in this article. The proposed dummy‐generation technique is a circle‐based technique which generates dummy locations in the circle area and is effective against center‐of‐anonymized spatial region attack, map‐matching attack, and location‐homogeneity attack. Additionally, an edge computing enabled framework is proposed, which can be used in the IoT environment. The edge computing enabled framework helps in handling the resource poverty issues of the service requesting devices and provides the low latency solution. The security analysis of our proposed dummy‐generation technique reflects that the proposed technique is resilient to specific attacks for different adversary attack models. The results obtained through simulations suggest that our technique performs better than the pre‐existing techniques.

Cloud computing provides computing resources, platforms, and applications as a service in a flexible, cost-effective, and efficient way. Cloud computing has integrated with industry and many other fields in recent years, which prompted researchers to look into new technologies. Cloud users have moved their applications, data and services to the Cloud storage due to the availability and scalability of Cloud services. Cloud services and applications are provided through the Internet-based on a pay-per-use model. Plenty of security issues are created due to the migration from local to remote computing for both Cloud users and providers. This paper discusses an overview of Cloud computing, as well as a study of security issues at various levels of Cloud computing. The article also provides a complete review of security issues with their existing solutions for a better understanding of specific open research issues.