Data Privacy Final Year Projects with Source Code

Byadmin Final Year Projects

Data Privacy Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Data Privacy projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Data Privacy Final Year Projects

  1. A Systematic Review on Federated Learning in Medical Image Analysis
    This project reviews how Federated Learning (FL) is used for analyzing medical images while keeping patient data private. The authors collected and studied research articles to understand how FL models perform compared to traditional methods. They summarized the current methods, results, challenges, and suggested directions for future research. Overall, it gives a clear picture of FL’s role in privacy-preserving medical AI.
  2. Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT
    This project focuses on making Internet of Things (IoT) devices more reliable and secure. It uses blockchain technology to protect sensitive data and a smart machine learning model to detect faults in IoT networks. The system also optimizes the model for better accuracy. Experiments show that this approach can detect faults with up to 99.6% accuracy, making IoT systems safer and more trustworthy.
  3. Holochain An Agent-Centric Distributed Hash Table Security in Smart IoT Applications
    This project studies a new technology called Holochain as an alternative to blockchain for securing Internet of Things (IoT) networks. It focuses on smart agriculture, where land records and data need protection from unauthorized access and corruption. Holochain allows peer-to-peer transactions with better scalability and local data storage compared to blockchain. The research explains its architecture, challenges, and how it can enable secure, distributed applications.
  4. PSDS–Proficient Security Over Distributed Storage A Method for Data Transmission in Cloud
    This project focuses on keeping data safe when stored in the cloud. It introduces a method called PSDS, which separates sensitive and normal data. Sensitive data is split, encrypted, and stored across multiple clouds, while normal data is stored in a single cloud. Tests show that PSDS protects against various cyberattacks and is faster than older encryption methods.
  5. A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle
    This paper studies how Artificial Intelligence (AI) systems handle personal data and the privacy risks involved. It explains that at each stage of an AI system—like collecting, cleaning, and using data—there can be threats to privacy. The study identifies four main privacy risks and reviews methods, technologies, and policies to protect data. It highlights challenges and solutions for keeping AI systems ethical and compliant with privacy rules.
  6. Implementing Privacy-Preserving and Collaborative Industrial Artificial Intelligence
    This project focuses on improving industrial AI for smart manufacturing. It uses a method called federated learning, which allows different factories or systems to train AI models together without sharing raw data, keeping it private and secure. The project also develops a framework to guide collaborative AI use in industry. Additionally, it provides a publicly available dataset for quality inspection to help further research.
  7. The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities A Stakeholders Perspective
    This project studies how artificial intelligence can help improve cybersecurity in government digital services. It looks at how AI and e-Governance together protect systems from cyber threats. The research also shows that the involvement of different stakeholders, like experts and officials, is important for stronger security. The findings help governments of smart cities make their online services safer and more reliable.
  8. Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking
    This project studies a way to train machine learning models without a central server, using devices that share updates with their neighbors. It focuses on keeping data private while still achieving high accuracy. The system is tested on the MNIST image dataset using a CNN model, and it shows over 95% accuracy. The method also reduces communication costs and trains faster compared to traditional centralized systems.
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At Final Year Projects, we provide complete guidance for Data Privacy IEEE projects for BE, BTech, ME, MSc, MCA and MTech students. We assist at every step from topic selection to coding, report writing, and result analysis.

Our team has over 10 years of experience guiding students in Computer Science, Electronics, Electrical, and other engineering domains. We support students across India, including Hyderabad, Mumbai, Bangalore, Chennai, Pune, Delhi, Ahmedabad, Kolkata, Jaipur and Surat. International students in the USA, Canada, UK, Singapore, Australia, Malaysia, and Thailand also benefit from our expert guidance.

Data Privacy Project Synopsis & Presentation

Final Year Projects helps prepare Data Privacy project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Data Privacy Project Thesis Writing

Final Year Projects provides thesis writing services for Data Privacy projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

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We accept Word, RTF, and LaTeX formats. Every paper is reviewed to meet IEEE and publication standards, improving acceptance chances. Our guidance ensures that students produce high-quality, publication-ready research papers.

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