{"title": "Fuzzy-Dollop", "logoImg": "bafkreig36rrwdjtixl3eua4hcgav3t4kwhbrtn3rnhylxhc3hyf34rc5ne", "website": "https://sukanyamandal.github.io/fuzzy-dollop.github.io/", "bannerImg": "bafkreihhgbbcbygkodyzi2fsf6z3zlzn4ktvkffnbavd6dpcfj7bogksya", "createdAt": 1717777522341, "userGithub": "sukanyamandal", "credentials": {}, "description": "# **Introduction:**\nImagine a world where our energy grids are not just smart but also seamlessly interconnected, optimising energy distribution in real-time while safeguarding user privacy. Our project, Empowering Smart Grids with Secure and Private Data Sharing, aims to revolutionise the way smart grids share and utilise data to create a more efficient, resilient, and sustainable energy landscape.\n\n# **The Problem:**\nToday’s energy grids face significant challenges. With the increasing adoption of renewable energy sources, the demand for efficient energy distribution has never been higher. However, traditional grids struggle with:\n\n* **Data Silos:** Energy data is often fragmented and isolated, leading to inefficiencies and a lack of real-time optimization.\n* **Privacy Concerns:** Sharing energy consumption data can expose sensitive information, deterring collaboration and innovation.\n* **Energy Wastage:** Inefficient energy distribution results in significant wastage, undermining sustainability efforts.\n\n\n# **Solution:**\nWe propose a cutting-edge platform that enables secure and private data sharing among smart grids, leveraging blockchain technology and privacy-preserving machine learning (ML).\n\n## **Key Components:**\n\n* **Blockchain for Secure and Immutable Data Sharing:**\n    * **Transparency and Security:** Blockchain ensures that all data shared among grids is secure, transparent, and tamper-proof, fostering trust and collaboration.\n    * **Immutable Records:** Every transaction and data exchange is recorded on the blockchain, providing a permanent and auditable trail.\n\n* **Federated Learning for Decentralised ML Training:**\n    * **Collaborative Intelligence:** Federated learning allows multiple smart grids to collaboratively train ML models without sharing raw data, retaining data privacy and security.\n    * **Decentralised Insights:** Each grid contributes to a global ML model, benefiting from collective intelligence while keeping sensitive data local.\n\n* **Privacy-Preserving Techniques:**\n    * **Differential Privacy:** Ensures that individual data points cannot be re-identified, adding an extra layer of privacy protection.\n    * **Homomorphic Encryption:** Allows computations to be performed on encrypted data, ensuring that sensitive information remains confidential even during processing.\n\n## **Sustainability Impact:**\n\nOur platform significantly enhances grid efficiency and sustainability by:\n\n* **Optimising Energy Distribution:** Real-time data sharing and advanced ML algorithms help balance supply and demand, reducing energy wastage.\n* **Reducing Carbon Footprint:** Improved efficiency means less reliance on non-renewable energy sources, promoting a greener energy ecosystem.\n* **Empowering Consumers:** By safeguarding privacy, we encourage more users to participate in energy-saving programs, further driving sustainability efforts.\n\n# Conclusion:\n\nBy integrating blockchain and privacy-preserving ML into smart grid data sharing, our platform addresses critical challenges in the energy sector. It fosters a collaborative environment where grids can share insights securely, optimise energy distribution, and enhance sustainability without compromising user privacy.\n\n\n## **Project Codebase:**\nhttps://github.com/sukanyamandal/fuzzy-dollop", "logoImgData": {}, "bannerImgData": {}, "projectTwitter": "msukanya93"}