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Dr.R.Karthick
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Page No: 1 - 11
Abstract : The growing demand for mobile video broadcasting has led to the widespread use of cloud-based platforms for video storage and streaming. However, concerns regarding user privacy and security are paramount, especially when dealing with sensitive or personal content. This paper proposes a novel framework for privacy-preserving mobile video broadcasting, incorporating secure trimmed video downloads via cloud-based infrastructure. The framework leverages advanced encryption and access control mechanisms to ensure that users can securely access and stream only the specific segments of videos they are authorized to view, preventing unauthorized access to full content. The proposed solution integrates dynamic video trimming at the server side, where users request and receive encrypted video segments according to their preferences. Additionally, the system uses a hybrid cryptographic approach, combining homomorphic encryption and attribute-based encryption to ensure secure video delivery, with the ability to protect both video content and user metadata. The framework’s efficiency is evaluated in terms of encryption overhead, download speed, and security, showing that it significantly improves privacy while maintaining a seamless user experience for mobile video broadcasting.
Keyword Privacy-preserving video broadcasting, Secure video downloads, Cloud-based video streaming, Video trimming, Homomorphic encryption, Attribute-based encryption, Mobile video privacy, Secure cloud video delivery, Video access control.
Reference:
  1. Srinivasan, R. (2025). Friction Stir Additive Manufacturing of AA7075/Al2O3 and Al/MgB2 Composites for Improved Wear and Radiation Resistance in Aerospace Applications. Environ. Nanotechnol, 14(1), 295-305.
  2. Deepa, R., Karthick, R., Velusamy, J., & Senthilkumar, R. (2025). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, 92, 103934.
  3. Vijayalakshmi, K., Amuthakkannan, R., Ramachandran, K., & Rajkavin, S. A. (2024). Federated Learning-Based Futuristic Fault Diagnosis and Standardization in Rotating Machinery. SSRG International Journal of Electronics and Communication Engineering, 11(9), 223-236.
  4. Rajakannu, A. (2024). Implementation of Quality Function Deployment to Improve Online Learning and Teaching in Higher Education Institutes of Engineering in Oman. International Journal of Learning, Teaching and Educational Research, 23(12), 463-486.
  5. Rajakannu, A., Ramachandran, K. P., & Vijayalakshmi, K. (2024). Application of Artificial Intelligence in Condition Monitoring for Oil and Gas Industries.
  6. Al Haddabi, T., Rajakannu, A., & Al Hasni, H. (2024). Design and Development of a Low-Cost Parabolic Type Solar Dryer and Its Performance Evaluation in Drying of King Fish–Case Study in Oman.
  7. Rajakannu, A., Ramachandran, K. P., & Vijayalakshmi, K. (2024). Condition Monitoring of Drill Bit for Manufacturing Sector Using Wavelet Analysis and Artificial Neural Network (ANN).
  8. Sakthibalan, P., Saravanan, M., Ansal, V., Rajakannu, A., Vijayalakshmi, K., & Vani, K. D. (2023). A Federated Learning Approach for ResourceConstrained IoT Security Monitoring. In Handbook on Federated Learning (pp. 131-154). CRC Press.
  9. Prova, N. N. I. (2024, August). Healthcare Fraud Detection Using Machine Learning. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) (pp. 1119-1123). IEEE.
  10. Prova, N. N. I. (2024, August). Advanced Machine Learning Techniques for Predictive Analysis of Health Insurance. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) (pp. 1166-1170). IEEE.
  11. Sidharth, S. (2023). AI-Driven Anomaly Detection for Advanced Threat Detection.
  12. Prova, N. N. I. (2024, August). Garbage Intelligence: Utilizing Vision Transformer for Smart Waste Sorting. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) (pp. 1213-1219). IEEE.
  13. Prova, N. N. I. (2025). Enhancing Agricultural Research with an Attention-Based Hybrid Model for Precise Classification of Rice Varieties. Authorea Preprints.
  14. Prova, N. N. I. (2024, October). Improved Solar Panel Efficiency through Dust Detection Using the InceptionV3 Transfer Learning Model. In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 260-268). IEEE.
  15. Sidharth, S. (2017). Real-Time Malware Detection Using Machine Learning Algorithms.
  16. Arun, R., Bhakar, S., Turlapati, V. R., Shanthi, P., & Saikumari, V. (2024). From Data to Decisions on Artificial Intelligence’s Influence on Digital Marketing Research. In Optimizing Intelligent Systems for Cross-Industry Application (pp. 1-18). IGI Global.
  17. Turlapati, V. R., Thirunavukkarasu, T., Aiswarya, G., Thoti, K. K., Swaroop, K. R., & Mythily, R. (2024, November). The Impact of Influencer Marketing on Consumer Purchasing Decisions in the Digital Age Based on Prophet ARIMA-LSTM Model. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-6). IEEE.
  18. Sidharth, S. (2019). Quantum-Enhanced Encryption Methods for Securing Cloud Data.
  19. Indoria, D., Dakshinamoorthy, B., Karthik, M., Sharma, M., Kaliappan, S., & Manikandan, G. (2024, December). Transforming HR in Finance by Leveraging IoT and AI for Strategic Talent Management. In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-6). IEEE.
  20. Wisetsri, W., Clingan, P., Dwyer, R. J., & Bakhronova, D. (Eds.). (2024). Emerging Trends in Smart Societies: Interdisciplinary Perspectives.
  21. Kumar, P., Indoria, D., Chanti, Y., Tayal, M., Singh, J., & Munagala, M. (2024, May). Enhancing Security for Online Transactions through Supervised Machine Learning in Credit Card Fraud Detection. In 2023 International Conference on Smart Devices (ICSD) (pp. 1-6). IEEE.
  22. Indoria, D., Singh, J., Garg, N., Tiwari, M., Karthik, B. N., & Shaik, N. (2024, March). Security Evaluation and Oversight in Stock Trading Using Artificial Intelligence. In International Conference on Innovation and Emerging Trends in Computing and Information Technologies (pp. 105-115). Cham: Springer Nature Switzerland.
  23. Devi, K., & Indoria, D. (2024). Impact of Russia-Ukraine War on the Financial Sector of India. Drishtikon: A Management Journal, 15(1).
  24. Indoria, D., Kiran, P. N., Kumar, A., Goel, M., Shelke, N. A., & Singh, J. (2023, November). Artificial intelligence and machine learning in human resource management and market research for enhanced effectiveness and organizational benefits. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 1135-1140). IEEE.
  25. Kalimuthu, S., Perumal, T., Yaakob, R., Marlisah, E., & Babangida, L. (2021, March). Human Activity Recognition based on smart home environment and their applications, challenges. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 815-819). IEEE.
  26. Vidhyasagar, B. S., Lakshmanan, A. S., Abishek, M. K., & Kalimuthu, S. (2023, October). Video captioning based on sign language using yolov8 model. In IFIP International Internet of Things Conference (pp. 306-315). Cham: Springer Nature Switzerland.
  27. Ramanujam, E., Kalimuthu, S., Harshavardhan, B. V., & Perumal, T. (2023, October). Improvement in Multi-resident Activity Recognition System in a Smart Home Using Activity Clustering. In IFIP International Internet of Things Conference (pp. 316-334). Cham: Springer Nature Switzerland.
  28. Vidhyasagar, B. S., Harshagnan, K., Diviya, M., & Kalimuthu, S. (2023, October). Prediction of Tomato Leaf Disease Plying Transfer Learning Models. In IFIP International Internet of Things Conference (pp. 293-305). Cham: Springer Nature Switzerland.
  29. Sidharth, S. (2022). Zero Trust Architecture: A Key Component of Modern Cybersecurity Frameworks.
  30. Vidhyasagar, B. S., Arvindhan, M., Arulprakash, A., Kannan, B. B., & Kalimuthu, S. (2023, November). The crucial function that clouds access security brokers play in ensuring the safety of cloud computing. In 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) (pp. 98-102). IEEE.
  31. Sidharth, S. (2018). Optimized Cooling Solutions for Hybrid Electric Vehicle Powertrains.
  32. Sivakumar, K., Perumal, T., Yaakob, R., & Marlisah, E. (2024, March). Unobstructive human activity recognition: Probabilistic feature extraction with optimized convolutional neural network for classification. In AIP Conference Proceedings (Vol. 2816, No. 1). AIP Publishing.
  33. Raja, D. R. K., Abas, Z. A., Kumar, G. H., Murthy, C. R., & Eswari, V. (2024). Hybrid optimization algorithm for resource-efficient and data-driven performance in agricultural IoT. TELKOMNIKA (Telecommunication Computing Electronics and Control), 23(1), 201-210.
  34. Kumar, G. H., Raja, D. K., Varun, H. D., & Nandikol, S. (2024, November). Optimizing Spatial Efficiency Through Velocity-Responsive Controller in Vehicle Platooning. In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS) (pp. 1-5). IEEE.
  35. Kumar, G. H., KN, V. S., Patil, P., Moinuddin, M., Faraz, M., & Kumar, Y. D. (2024, September). Human-Computer Interaction for Drone Control through Hand Gesture Recognition with MediaPipe Integration. In 2024 International Conference on Vehicular Technology and Transportation Systems (ICVTTS) (Vol. 1, pp. 1-6). IEEE.
  36. Kumar, G. H., Raja, D. K., Suresh, S., Kottamala, R., & Harsith, M. (2024, August). Vision-Guided Pick and Place Systems Using Raspberry Pi and YOLO. In 2024 2nd International Conference on Networking, Embedded and Wireless Systems (ICNEWS) (pp. 1-7). IEEE.
  37. Sidharth, S. (2020). The Rising Threat of Deepfakes: Security and Privacy Implications.
  38. Raja, D. K., Abas, Z., Eswari, V., Kumar, G. H., & Kalpanad, V. (2024). Integrating RFID Technology with Student Information Systems. High Performance Computing, Smart Devices and Networks, 125.
  39. Kumar Raja, D. R., Abas, Z., Eswari, V., Hemanth Kumar, G., & Kalpana, V. (2023, December). Integrating RFID Technology with Student Information Systems for Enhanced Management of Attendance and Financial Records. In International Conference on Computer Vision, High-Performance Computing, Smart Devices, and Networks (pp. 125-135). Singapore: Springer Nature Singapore.
  40. Sidharth, S. (2024). Strengthening Cloud Security with AI-Based Intrusion Detection Systems.
  41. Seshanna, M., Kumar, H., Seshanna, S., & Alur, N. (2021). THE INFLUENCE OF FINANCIAL LITERACY ON COLLECTIBLES AS AN ALTERNATIVE INVESTMENT AVENUE: EFFECTS OF FINANCIAL SKILL, FINANCIAL BEHAVIOUR AND PERCEIVED KNOWLEDGE ON INVESTORS’FINANCIAL WELLBEING. Turkish Online Journal of Qualitative Inquiry, 12(4).
  42. Rao, P. S. (2008). International Business Environment. HIMALAYA PUBLISHING HOUSE 2nd Rev. ed..
  43. Sreekanthaswamy, N., Anitha, S., Singh, A., Jayadeva, S. M., Gupta, S., Manjunath, T. C., & Selvakumar, P. (2025). Digital Tools and Methods. Enhancing School Counseling With Technology and Case Studies, 25.
  44. Sidharth, S. (2016). The Role of Artificial Intelligence in Enhancing Automated Threat Hunting 1Mr. Sidharth Sharma.
  45. Sreekanthaswamy, N., & Hubballi, R. B. (2024). Innovative Approaches To Fmcg Customer Journey Mapping: The Role Of Block Chain And Artificial Intelligence In Analyzing Consumer Behavior And Decision-Making. Library of Progress-Library Science, Information Technology & Computer, 44(3).
  46. Kalluri, S. V. S., & Narra, S. (2024). Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing. vol, 9, 6.
  47. Kalluri, V. S. Optimizing Supply Chain Management in Boiler Manufacturing through AI-enhanced CRM and ERP Integration. International Journal of Innovative Science and Research Technology (IJISRT).
  48. Kalluri, V. S. Impact of AI-Driven CRM on Customer Relationship Management and Business Growth in the Manufacturing Sector. International Journal of Innovative Science and Research Technology (IJISRT).
  49. Sidharth, S. (2017). Cybersecurity Approaches for IoT Devices in Smart City Infrastructures.
  50. Sidharth, S. (2019). DATA LOSS PREVENTION (DLP) STRATEGIES IN CLOUD-HOSTED APPLICATIONS.
  51. Kalaiselvi, B., & Thangamani, M. (2020). An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques. Measurement, 162, 107885.
  52. Prabhu Kavin, B., Karki, S., Hemalatha, S., Singh, D., Vijayalakshmi, R., Thangamani, M., … & Adigo, A. G. (2022). Machine learning‐based secure data acquisition for fake accounts detection in future mobile communication networks. Wireless Communications and Mobile Computing, 2022(1), 6356152.
  53. Geeitha, S., & Thangamani, M. (2018). Incorporating EBO-HSIC with SVM for gene selection associated with cervical cancer classification. Journal of medical systems, 42(11), 225.
  54. Kumar, J. S., Archana, B., Muralidharan, K., & Kumar, V. S. (2025). Graph Theory: Modelling and Analyzing Complex System. Metallurgical and Materials Engineering, 31(3), 70-77.
  55. Anandasubramanian, C. P., & Selvaraj, J. (2024). NAVIGATING BANKING LIQUIDITY-FACTORS, CHALLENGES, AND STRATEGIES IN CORPORATE LOAN PORTFOLIOS. Tec Empresarial, 6(1).
  56. Madem, S., Katuri, P. K., Kalra, A., & Singh, P. (2023, May). System Design for Financial and Economic Monitoring Using Big Data Clustering. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)(pp. 1-7). IEEE.
  57. Srikanth, V., & Dhanapal, D. R. (2012). E-commerce online security and trust marks. International Journal of Computer Engineering and Technology, 3(2), 238-255.
  58. Srikanth, V., Walia, R., Augustine, P. J., Simla, J., & Jegajothi, B. (2022, March). Chaotic Whale Optimization based Node Localization Protocol for Wireless Sensor Networks Enabled Indoor Communication. In 2022 International Conference on Electronics and Renewable Systems (ICEARS)(pp. 702-707). IEEE.
  59. Srikanth, V., Natarajan, V., Jegajothi, B., Arumugam, S. D., & Nageswari, D. (2022, March). Fruit fly optimization with deep learning based reactive power optimization model for distributed systems. In 2022 International Conference on Electronics and Renewable Systems (ICEARS)(pp. 319-324). IEEE.
  60. Singh, S., Srikanth, V., Kumar, S., Saravanan, L., Degadwala, S., & Gupta, S. (2022, February). IOT Based Deep Learning framework to Diagnose Breast Cancer over Pathological Clinical Data. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)(Vol. 2, pp. 731-735). IEEE.
  61. Srikanth, V., & Dhanapal, R. (2011). A business review of e-retailing in India. International journal of business research and management, 1(3), 105-121.
  62. Srikanth, V. (2011). An Insight to Build an E-Commerce Website with OSCommerce. International Journal of Computer Science Issues (IJCSI), 8(3), 332.
  63. Srikanth, V., Aswini, P., Asha, V., Pithamber, K., Sobti, R., & Salman, Z. (2024, November). Development of an Electric Automation Control Model Using Artificial Intelligence. In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES)(pp. 1-5). IEEE.
  64. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust Node Localization with Intrusion Detection for Wireless Sensor Networks. Intelligent Automation & Soft Computing, 33(1).
  65. Srikanth, V., Aswini, P., Chandrashekar, R., Sirisha, N., Kumar, M., & Adnan, K. (2024, November). Machine Learning-Based Analogue Circuit Design for Stage Categorization and Evolutionary Optimization. In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES)(pp. 1-6). IEEE.
  66. Lopez, S., Sarada, V., Praveen, R. V. S., Pandey, A., Khuntia, M., & Haralayya, D. B. (2024). Artificial intelligence challenges and role for sustainable education in india: Problems and prospects. Sandeep Lopez, Vani Sarada, RVS Praveen, Anita Pandey, Monalisa Khuntia, Bhadrappa Haralayya (2024) Artificial Intelligence Challenges and Role for Sustainable Education in India: Problems and Prospects. Library Progress International, 44(3), 18261-18271.
  67. Yamuna, V., Praveen, R. V. S., Sathya, R., Dhivva, M., Lidiya, R., & Sowmiya, P. (2024, October). Integrating AI for Improved Brain Tumor Detection and Classification. In 2024 4th International Conference on Sustainable Expert Systems (ICSES)(pp. 1603-1609). IEEE.
  68. Kumar, N., Kurkute, S. L., Kalpana, V., Karuppannan, A., Praveen, R. V. S., & Mishra, S. (2024, August). Modelling and Evaluation of Li-ion Battery Performance Based on the Electric Vehicle Tiled Tests using Kalman Filter-GBDT Approach. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS)(pp. 1-6). IEEE.
  69. Sharma, S., Vij, S., Praveen, R. V. S., Srinivasan, S., Yadav, D. K., & VS, R. K. (2024, October). Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models. In 2024 4th International Conference on Sustainable Expert Systems (ICSES)(pp. 1631-1636). IEEE.
  70. Anuprathibha, T., Praveen, R. V. S., Sukumar, P., Suganthi, G., & Ravichandran, T. (2024, October). Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings. In 2024 Global Conference on Communications and Information Technologies (GCCIT)(pp. 1-5). IEEE.
  71. Shinkar, A. R., Joshi, D., Praveen, R. V. S., Rajesh, Y., & Singh, D. (2024, December). Intelligent solar energy harvesting and management in IoT nodes using deep self-organizing maps. In 2024 International Conference on Emerging Research in Computational Science (ICERCS)(pp. 1-6). IEEE.
  72. Praveen, R. V. S., Hemavathi, U., Sathya, R., Siddiq, A. A., Sanjay, M. G., & Gowdish, S. (2024, October). AI Powered Plant Identification and Plant Disease Classification System. In 2024 4th International Conference on Sustainable Expert Systems (ICSES)(pp. 1610-1616). IEEE.
  73. Ramesh, T. R., Lilhore, U. K., Poongodi, M., Simaiya, S., Kaur, A., & Hamdi, M. (2022). Predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132-148.
  74. Ramesh, T. R., Vijayaragavan, M., Poongodi, M., Hamdi, M., Wang, H., & Bourouis, S. (2022). Peer-to-peer trust management in intelligent transportation system: An Aumann’s agreement theorem based approach. ICT Express, 8(3), 340-346.
  75. Ramesh, T. R., & Kavitha, C. (2013). Web user interest prediction framework based on user behavior for dynamic websites. Life Sci. J, 10(2), 1736-1739.
  76. Jayapandiyan, J. R., Kavitha, C., & Sakthivel, K. (2020). Enhanced least significant bit replacement algorithm in spatial domain of steganography using character sequence optimization. Ieee Access, 8, 136537-136545.
  77. Sakthivel, K., Jayanthiladevi, A., & Kavitha, C. (2016). Automatic detection of lung cancer nodules by employing intelligent fuzzy c-means and support vector machine. BIOMEDICAL RESEARCH-INDIA, 27, S123-S127.
  78. Sakthivel, K., Nallusamy, R., & Kavitha, C. (2014). Color image segmentation using SVM pixel classification image. World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering, 8(10), 1924-1930.
  79. Hussain, M. I., Shamim, M., Ravi Sankar, A. V., Kumar, M., Samanta, K., & Sakhare, D. T. (2022). The effect of the Artificial Intelligence on learning quality & practices in higher education. Journal of Positive School Psychology, 1002-1009.
  80. Prasad, V., Dangi, A. K., Tripathi, R., & Kumar, N. (2023). Educational Perspective of Intellectual Property Rights. Russian Law Journal, 11(2S), 257-268.
  81. Shreevamshi, D. V. K., Jadhavar, S. S., Vemuri, V. P., & Kumar, A. (2022). Role Of Green HRM in Advocating Pro-Environmental Behavior Among Employees. Journal of Positive School Psychology, 6(2), 3117-3129.
  82. Somasundaram, R., Chandra, S., Tamilarasu, J., Kinagi, A. M., & Naveen, S. (2025). Human Resource Development (HRD) Strategies for Emerging Entrepreneurship: Leveraging UX Design for Sustainable Digital Growth. In Navigating Usability and User Experience in a Multi-Platform World(pp. 221-248). IGI Global.

Privacy-Preserving Mobile Video


The rapid proliferation of mobile devices and high-speed internet connectivity has transformed. Mobile video broadcasting has become a dominant
form of communication, enabling users to stream, upload, and share videos in real-time through
various platforms such as YouTube, TikTok, and social media applications. Simultaneously, cloud
computing has revolutionized content delivery by offering scalable storage, bandwidth management,
and on-demand video streaming services. However, this increasing reliance on cloud platforms has
also introduced critical challenges related to data privacy, security, and unauthorized access to
sensitive video content Privacy-Preserving Mobile Video.

Privacy-Preserving Mobile Video


In conventional video broadcasting systems, videos uploaded to cloud servers are typically stored and
streamed in full, allowing users to view the entire content. While this is efficient in terms of
accessibility, it raises significant privacy concerns, especially when only a portion of the video needs
to be accessed or shared. For example, in medical consultations, educational settings, surveillance
footage, or personal recordings, users may want to share specific segments without exposing the
entire video. This scenario necessitates a privacy-preserving mechanism that supports selective access
to video segments while ensuring that both the content and associated metadata remain protected.
One of the core issues in mobile video broadcasting lies in the lack of secure and fine-grained control
over video content at the segment level. Looking ahead, several enhancements can be explored to improve the system’s capabilities and
scalability. Future work can focus on incorporating machine learning-based video content classifiers
that can automatically detect and redact sensitive content before broadcasting, further improving
privacy assurance.

To address these concerns, this paper proposes a privacy-preserving mobile video broadcasting
framework that enables secure trimmed video downloads from cloud platforms. The framework
employs a hybrid encryption model that integrates homomorphic encryption and attribute-based
encryption (ABE) to secure both video content and user attributes.

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Privacy-Preserving Mobile Video FIGURE 1. Privacy-Preserving Surveillance as an Edge Service Based on Lightweight Video Protection Schemes Using Face De-Identification and Window Masking.