Akhila Reddy Yadulla

To address the challenges of data security governance under new conditions and align with technological trends in data security, this paper proposes a graph algorithm-based method for identifying key personnel with critical permissions in the practical applications of large state-owned enterprises. This method can uncover potential permission influence factors within the system and evaluate the weight of influence from different perspectives, providing highly interpretable identification results. To tackle the issue of detecting anomalous user and entity behaviors in data security governance, a user and entity behavior anomaly detection method based on Generative Adversarial Networks (GAN) is introduced. Experimental results show that the proposed method achieves higher precision, recall, and F1-score averages compared to baseline models; specifically, an average F1-score of 0.75 versus 0.72 for LSTM-based TadGAN, 0.62 for ARIMA, and 0.65 for a commercial UEBA baseline across the three evaluation datasets. A data security platform was designed and developed, which plays a significant role in reducing data security risks, assisting enterprise compliance, and promoting data development and utilization. This platform has been applied in various centralized data management projects and meets the big data processing requirements in secure environments, demonstrating strong application and promotional value.

A new design of privacy computing gateway stands as the solution to secure efficient interoperability between heterogeneous platforms. The growing importance of data privacy, along with rising collaborative data analysis operations, creates an immediate need for standardized privacy-preserving frameworks that are adaptable to diverse situations. A three-layered architecture consisting of application protocol and communication layers receives support from an Adaptation mechanism designed for compatibility between separate privacy computing systems. Testing of the framework uses standard machine learning methods together with horizontal and vertical federated learning using diverse data quantities and feature distribution patterns. The gateway achieves satisfactory model performance and protects data privacy integrity in combination with platform interoperability. area under the curve (AUC) along with F1 score metrics, proves that the proposed system reaches performance equivalence with centralized models when operating within privacy-limited environments. The research introduces an effective solution for securing cross-platform data sharing that will enable secure inter-sector collaboration in finance, healthcare, and government applications.

Blockchain technology allows decentralized cryptocurrencies to change digital finances by providing secure, pseudonymous transactions to users. Since blockchain ledgers operate in a public environment, users can face potential privacy risks due to the exposure of their transaction patterns. Conventional cryptocurrency systems use block generation for transaction confirmation, yet this process produces latency and impacts the real-time efficiency of transactions. This paper develops a proxy-assisted cryptocurrency payment system that employs blind signature principles to achieve better system privacy and enhanced speed. The core functionality of this proposed system aims to protect transaction secrecy as it speeds up confirmation processes. A proxy node handles transaction requests through blind signature protocols that guarantee data confidentiality as part of the methodology. The proposed system utilizes deep learning tools, which include recurrent neural networks (RNN), graph neural networks (GNN), and reinforcement learning (RL) to forecast confirmation results, identify scams, and control proxy functions dynamically. Research indicates that the introduced method substantially boosts privacy features, decreases transaction latencies, and enhances the security of all transactions by providing an encouraging roadmap for secure cryptocurrency systems that preserve privacy.

The quick advancement of big data sharing increases the importance of protecting data privacy. Differential privacy appeared in 2006 to become the fundamental privacy-preserving data mining and publication method. Various technologies, including federated learning, homomorphic encryption, and secure multi-party computation, have introduced new solutions for safeguarding data privacy during recent technological developments. Differential privacy is experiencing a comparison test in current technology markets because Google and Apple have integrated it into new products. This chapter begin by examining the differential privacy systems used in traditional centralized data models and their usage in data mining and publishing processes. The chapter investigates modern developments in local differential privacy by examining randomized response Bloom filters and statistical inference tools related to crowdsourcing systems.

Electroencephalography (EEG) is a widely used non-invasive method for capturing brain activity, offering valuable insights into cognitive and emotional states relevant to mental health. With the growing complexity and volume of EEG data, machine learning (ML) techniques—particularly deep learning—have become integral in extracting meaningful patterns. While much of the current literature focuses on supervised learning methods that rely on labeled data, unsupervised learning offers an alternative approach capable of discovering hidden structures and novel biomarkers without requiring predefined labels. This systematic review aimed to identify and synthesize recent peer-reviewed research that applied unsupervised or self-supervised learning techniques to EEG data in the context of mental health monitoring, diagnosis, or analysis. A comprehensive search was conducted across six major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, and Google Scholar, covering literature from January 2018 to March 2025. Following PRISMA guidelines, predefined inclusion and exclusion criteria were applied to screen and assess the relevance and quality of studies. From 512 initial records, 403 unique articles were screened, and 20 underwent full-text review. Ultimately, no studies met all the inclusion criteria. Most were excluded for employing only supervised methods, being review articles, or focusing on non-mental-health applications. The absence of eligible studies highlights a significant gap in current research and emphasizes the need for future empirical work exploring unsupervised techniques in EEG-based mental health applications. Such efforts could pave the way for more scalable, label-free approaches to understanding brain dynamics in psychological conditions.