Abstract—. Privacy-preserving data publishing has become vital in the age of data-driven decision-making, especially with the increasing availability of unstructured datasets. While the Score, Arrange, and Cluster (SAC) algorithm effectively anonymizes structured data, it does not address the challenges posed by unstructured text data. This proposal introduces an enhanced SAC algorithm in corporating embedding techniques, semantic generalization, and clustering to efficiently process and anonymize text data. The proposed system computes embeddings for textual attributes, creates semantic hierarchies for generalization, and clusters similar records to achieve k-anonymity. This approach ensures privacy preservation while retaining the utility of the transformed data, making it suitable for various privacy-sensitive applications.
Keywords- k-anonymity, Generalization, Clustering, Privacy Sensitivity Applications, Semantic Embeddings.
K. G, Kiran B. M., G. Prasad· International Scientific Jou...· 0 citations
Abstract— This research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest – 95% of overall accuracy as opposed to 92%. It is particularly important to consider AUC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization – features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats.
Keywords- Cloud Data Security, Machine Learning, Random Forest, Deep Neural Networks, Q-Learning
Bharda Priya Dutt, Kiran B. M., G. Prasad· International Scientific Jou...· 0 citations
ABSTRACT - Rumours and misinformation propagate rapidly across online social networks, posing significant challenges to maintaining the integrity of information dissemination. In recent years, machine learning (ML) techniques have emerged as promising tools for automating the detection and mitigation of rumours. This review paper provides a comprehensive examination of the advancements in rumour detection using ML approaches. The paper begins by outlining the landscape of rumour dissemination in online social networks, highlighting the characteristics and challenges associated with rumour detection. Subsequently, it systematically categorizes and analyzes various ML methods employed for rumour detection, including supervised, unsupervised, and semi-supervised learning approaches. Furthermore, the review delves into the diverse features and representations utilized in ML models for rumour detection, such as textual content, user engagement patterns, network structures, and temporal dynamics. It discusses the strengths and limitations of different feature sets and their impact on the effectiveness of rumour detection systems. Moreover, the paper explores the intricacies of dataset construction and evaluation methodologies for training and testing rumour detection models. It examines commonly used benchmark datasets and evaluation metrics, emphasizing the importance of robust evaluation frameworks for assessing the performance of ML-based rumour detection systems accurately. Additionally, the review identifies key challenges and open research questions in the field of rumour detection using ML, including handling evolving rumour patterns, addressing adversarial attacks, and enhancing the interpretability and explain ability of ML models. It also discusses potential directions for future research aimed at advancing the state-of-the-art in rumour detection and mitigation.
C.Sandeep Reddy, Kiran B. M., P. Rani· International Scientific Jou...· 0 citations
This project presents an ML-Based Real-Time UPI Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions efficiently and shows that the Random Forest algorithm achieves the highest accuracy, making it the most effective model for fraud detection.
Yekkirala Suvarcha, K. M., G. Prasad· International Scientific Jou...· 0 citations
An optimized machine learning-based Air Quality Forecasting System that integrates Extreme Learning Machines (ELM) and Genetic Algorithms (GA) to predict short-term variations in air quality and demonstrates a robust, scalable, and practical solution for short-term air quality prediction.
Shivatejaswini B, Kiran B. M., G. Prasad· International Scientific Jou...· 0 citations
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