Prakash Tekchandani supervised by Prof. Ashok Kumar Das received  his doctorate in  Computer Science and Engineering (CSE). Here’s a  summary of his research work on Design and Implementation of Secure Big Data Analytics Mechanisms for Internet of Things Applications:

The exponential growth of big data, driven by rapid technological advancements, has transformed the landscape of data analysis and business decision-making. The integration of data from the Internet of Things (IoT) devices, healthcare, manufacturing, banking, and other applications has opened up new avenues for deriving valuable insights and enhancing business operations. Organizations leverage powerful analytics to drive strategic decisions, identify opportunities, and improve performance. However, the vast increase in data usage and complexity brings significant security and privacy challenges. Big data relies heavily on infrastructure, cloud, and edge computing, all of which pose inherent security risks. Untrusted applications and insufficient security measures can introduce vulnerabilities into enterprise systems. Additionally, the widespread use of mobile and IoT devices increases the risk of unauthorized access and misuse of sensitive data. Enterprises must prioritize defining secure access privileges and implementing robust security protocols to prevent potential data misuse. As data sharing and source authentication become more prevalent, trust between parties remains a critical issue. Governments, for instance, are cautious about sharing data due to national security concerns, while individuals worry about the privacy of their personal information. The involvement of multiple stakeholders often leads to poor control over data transfer, hindering the development of the big data industry. The presence of personally identifiable information (PII), trade secrets, and other sensitive details raises significant privacy concerns. Balancing the utility of data analysis with the protection of individual privacy requires employing various cryptographic, anonymization, and access control methods. Compliance with privacy regulations, such as the “General Data Protection Regulation (GDPR) in the European Union”, is crucial for organizations handling big data. Non-compliance can lead to severe legal and financial consequences. Therefore, secure big data analytics requires secure data transfer and privacy-preserving that necessitates a combination of technical, organizational, and legal measures to ensure that individual privacy is protected while still enabling the derivation of valuable insights from large-scale data analysis. Thus, the ongoing challenge of secure big data analytics demands innovative solutions that can effectively secure data with privacy protection. Existing methods often struggle with complexity, scalability, and the ability to ensure secure big data analytics while maintaining privacy and confidentiality of data. Organizations must continuously adapt and enhance their strategies to keep pace with the evolving landscape of big data and its associated risks. The first study in this thesis presents a comprehensive approach that utilizes data of smart devices in IoT. This data is crucial for building intelligent applications using machine learning. Typically, data from these devices is collected into data centers for training machine learning models. The traditional centralized training approach, which requires transferring data from devices to a central server, is inefficient due to privacy concerns and bandwidth limitations, as users are often reluctant to share their data with centralized data centers. To address these issues, we propose an efficient hybrid secure federated learning approach integrated with blockchain technology. This method allows models to be securely trained locally on devices, with the model and its parameters stored in the blockchain for traceability and immutability. A detailed security and performance analysis demonstrates the efficacy of this proposed approach, highlighting its security, resilience against numerous attacks, and cost-effectiveness in computation and communication compared to existing schemes. We introduce a secured federated learning approach for big data analytics based on an authenticated key agreement protocol and blockchain. We utilize the “Genetic Algorithm Neural Network (GANN)” to train our model and propose a secure and efficient authentication and key agreement scheme to ensure the secure exchange of model parameters in federated learning. The transactions created as model parameters among IoT smart devices are securely stored in blocks and added to the blockchain using the “Practical Byzantine Fault Tolerance (PBFT)” consensus protocol. Experiments and performance analysis of our proposed machine learning model indicate its efficiency under various attack scenarios. Additionally, a detailed formal security analysis and verification using the widely-used Scyther simulation tool show that the proposed scheme is resilient against several attacks common in the IoT environment. A rigorous comparative study confirms that the proposed scheme offers an accurate, secure, and resilient federated machine learning model for big data analytics. The second study in this thesis presents a novel blockchain-enabled secured collaborative machine learning framework that ensures both privacy and confidentiality for large-scale datasets generated by IoT devices. Blockchain serves as a secure platform for data storage and access, providing immutability and traceability. Our proposed approach combines cryptography and differential privacy to achieve a robust machine learning model, where data is shared securely among parties while maintaining privacy and confidentiality. Experimental evaluation, along with security and performance analysis, demonstrates that the proposed approach provides accuracy and scalability without compromising privacy and security. Specifically, we develop a secured collaborative model learning approach for big data analytics based on differential privacy and blockchain. We use Automated Machine Learning (AutoML) to train our model and implement secure and efficient collaborative learning to ensure data privacy. Critical parameters generated during the execution of the proposed scheme are securely stored in the blockchain using the Hyperledger Sawtooth framework. Experiments conducted with our proposed approach show that it is accurate and prevents the disclosure of private data. Detailed security analysis indicates that the proposed scheme is resilient against various threats in machine learning. Performance analysis reveals that our scheme is fast, efficient, and less prone to errors. A rigorous experimental, analytical, and comparative study shows that the proposed scheme provides an efficient and secure collaborative machine learning model for big data analytics, maintaining both privacy and confidentiality of data. The last, but not the least, third study aims in proposing a secure collaborative model learning methodology trained on synthetic data, ensuring data availability, privacy and confidentiality through differential privacy and key management. IoT realm demands a substantial volume of data for training models and making reliable inferences. In most cases, data availability is scarce, and synthetic data is generated from real-world data to meet the needs. Yet, there remains a risk of exposing private and sensitive information without proper data security measures. In this work, we propose a secured inference framework where user data, sent for inference to the deployed model is protected, preserving both the accuracy of the predicted data and the security of the input data. Our experimental evaluation, along with performance and security analysis, exhibits that our approach offers accuracy and scalability while maintaining privacy and security.

June 2025