Surveillance systems sensitive to privacy in AI based smart cities
Keywords:
AI, smart cities, surveillance, privacyAbstract
Cities grow day by day, making public safety, transportation congestion, and resource management difficult. Smart cities can address these challenges, but monitoring smart cities raises severe privacy concerns. Using AI for real time anonymizing and data monitoring can help in securing personal data and enhancing surveillance system. This paper explains the ethics, AI based smart city privacy sensitive surveillance system, privacy preserving technologies, monitoring system.
Downloads
References
Bardhan, S., & Cho, E. (2021). Data anonymization techniques for AI-based surveillance systems. International Journal of Artificial Intelligence in Urban Security, 5(2), 50-64.
Sivaraman, Hariprasad. "Intelligent Code Coverage Optimization Using Machine Learning for Large Scale Systems." International Journal for Multidisciplinary Research 5.5 (2023).
S. Kumari, “Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response ”, Adv. in Deep Learning Techniques, vol. 3, no. 2, pp. 50–74, Dec. 2023
V. Pillai, “Anomaly Detection in Financial and Insurance Data-Systems”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 144–183, Sep. 2024
Singu, Santosh Kumar. "Migration strategies for legacy data warehousing systems to cloud platforms." Internafional Journal of Science and Research (IJSR) 12, no. 12 (2023): 2164-2167.
Tamanampudi, Venkata Mohit. "AI Agents in DevOps: Implementing Autonomous Agents for Self-Healing Systems and Automated Deployment in Cloud Environments." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 507-556.
Machireddy, Jeshwanth Reddy, and Harini Devapatla. "Enhancing Predictive Analytics with AI-Powered RPA in Cloud Data Warehousing: A Comparative Study of Traditional and Modern Approaches." Journal of Deep Learning in Genomic Data Analysis 3.1 (2023): 74-99.
Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
S. Kumari, “Leveraging AI for Cybersecurity in Agile Cloud-Based Platforms: Real-Time Anomaly Detection and Threat Mitigation in DevOps Pipelines”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 698–715, May 2023
Alam, Khorshed, et al. "Designing Autonomous Carbon Reduction Mechanisms: A Data-Driven Approach in Renewable Energy Systems." Well Testing Journal 32.2 (2023): 103-129.
Sivaraman, Hariprasad. (2023). A Machine Learning Paradigm for Cross-Sector Financial Crime Prevention. 14.
Ravichandran, Prabu, Jeshwanth Reddy Machireddy, and Sareen Kumar Rachakatla. "Data Analytics Automation with AI: A Comparative Study of Traditional and Generative AI Approaches." Journal of Bioinformatics and Artificial Intelligence 3.2 (2023): 168-190.
Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
S. Kumari, “AI-Driven Product Management Strategies for Enhancing Customer-Centric Mobile Product Development: Leveraging Machine Learning for Feature Prioritization and User Experience Optimization ”, Cybersecurity & Net. Def. Research, vol. 3, no. 2, pp. 218–236, Nov. 2023.
Pillai, Vinayak. “Implementing Efficient Data Operations: An Innovative Approach”. Asian Journal of Multidisciplinary Research & Review, vol. 3, no. 6, Dec. 2022, pp. 231-67, https://ajmrr.org/journal/article/view/241.
Al Imran, Md, Abdullah Al Fathah, Abdullah Al Baki, Khorshed Alam, Md Ali Mostakim, Upal Mahmud, and M. S. Hossen. "Integrating IoT and AI For Predictive Maintenance in Smart Power Grid Systems to Minimize Energy Loss and Carbon Footprint." Journal of Applied Optics 44, no. 1 (2023): 27-47.
Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.
S. Kumari, “AI-Powered Agile Project Management for Mobile Product Development: Enhancing Time-to-Market and Feature Delivery Through Machine Learning and Predictive Analytics”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 342–360, Dec. 2023
Alam, K., M. A. Mostakim, A. A. Baki, and M. S. Hossen. "CURRENT TRENDS IN PHOTOVOLTAIC THERMAL (PVT) SYSTEMS: A REVIEW OF TECHNOLOGIES AND SUSTAINABLE ENERGY SOLUTIONS." Academic Journal on Business Administration, Innovation & Sustainability 4, no. 04 (2024): 128-143.
Pillai, V. “Data Analytics and Engineering in Automobile Data Systems”. Journal of Science & Technology, vol. 4, no. 6, Dec. 2023, pp. 140-79, https://thesciencebrigade.com/jst/article/view/520
Bellini, P., & Sutherland, T. (2020). Smart city surveillance: Leveraging AI for public safety while respecting privacy. Journal of Urban Technology, 16(3), 45-59.
Chen, J., & Kim, M. (2022). Machine learning and surveillance systems in urban environments. Journal of AI and Privacy, 14(1), 102-114.
D'Andrea, F., & Thompson, L. (2021). Federated learning for privacy-preserving smart city surveillance. AI & Ethics, 9(4), 142-156.
Farrell, B., & Harris, K. (2021). The role of AI in enhancing privacy in urban surveillance systems. Privacy and Data Protection Review, 12(2), 99-112.
Garcia, L., & Walker, R. (2020). Legal frameworks for AI surveillance in smart cities. International Journal of Cyber Law, 6(1), 33-47.
