MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202641078189 A) filed by Madhankumar C; Dr. Yogesh Golhar; Dr. Ujwala Gawande; Dr. Sachin P. Nagmote; Dr. Roshan Kotkondawar; Dhirajkumar Gupta; Dr. Amit Thakare; and Dr. Dinesh Suryakant Wankhede on June 24, 2026, for Computer-Implemented Method For Efficient Privacy-Preserving Data Processing In Edge Computing Devices.
Inventors include Dr. Yogesh Golhar; Dr. Ujwala Gawande; Dr. Sachin P. Nagmote; Dr. Roshan Kotkondawar; Dhirajkumar Gupta; Dr. Amit Thakare; and Dr. Dinesh Suryakant Wankhede.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: Computer-Implemented Method for Efficient Privacy-Preserving Data Processing in Edge Computing Devices Abstract The present invention discloses a computer-implemented method for efficient privacy-preserving data processing in edge computing devices, designed to enhance data security, reduce latency, and optimize computational efficiency in distributed edge environments. The proposed method enables edge devices to collect, preprocess, encrypt, and analyze sensitive data locally without transmitting raw information to centralized cloud servers. The system integrates lightweight encryption techniques, federated learning mechanisms, differential privacy algorithms, and adaptive resource management to ensure secure data processing while maintaining low computational overhead. A privacy-aware data aggregation framework is employed to facilitate collaborative model training among multiple edge nodes without exposing individual user data. The method further incorporates intelligent workload scheduling and dynamic energy optimization to improve processing performance and device longevity. By minimizing data transmission, the invention significantly reduces network congestion, communication costs, and privacy risks associated with cloud-centric architectures. The proposed approach is applicable to Internet of Things (IoT) networks, smart healthcare systems, industrial automation, smart cities, autonomous vehicles, and real-time monitoring applications. Experimental evaluations demonstrate improved privacy protection, faster response times, enhanced energy efficiency, and higher scalability compared to conventional edge computing frameworks. The invention provides a secure, reliable, and efficient solution for next-generation edge intelligence systems requiring privacy preserving data analytics and decentralized decision-making.
Disclaimer: Curated by HT Syndication.
