What is Cifer?
Cifer is an open-source platform that seamlessly integrates blockchain technology with advanced privacy-preserving machine learning frameworks. At its core, Cifer is designed to address the challenges of data privacy, security, and decentralized collaboration in AI development.
We innovate new consensus mechanisms to enable machine learning to work harmoniously with blockchain, unlocking the full potential of decentralized machine learning. Developers can choose to develop decentralized applications, become node operators, and build AI using the most advanced, secure, private, and collaborative techniques. Our machine learning frameworks include Federated Learning, Swarm Intelligence, Fully Homomorphic Encryption, Multi-Party Computation, Multi-Agent Systems, and Data Tamper-Proof Systems.
2 Layers of Technology
Layer 1 - Byzantine Robust Blockchain Network
At the foundation of Cifer lies our Byzantine Robust Blockchain Network, a high-speed, secure, and resilient blockchain infrastructure designed to support a wide range of decentralized applications and services. This layer ensures the integrity, transparency, and security of all transactions and operations on the Cifer platform. By leveraging advanced consensus mechanisms and robust cryptographic techniques, our blockchain network provides a tamper-proof ledger that is resistant to malicious attacks and faults. Key features include:
Advanced Consensus Mechanism: Ensures efficient and secure transaction validation.
High Throughput: Supports a high volume of transactions with minimal latency.
Fault Tolerance: Maintains network integrity even in the presence of malicious nodes.
Decentralization: Promotes a distributed network that enhances security and reliability.
Layer 2 - Machine Learning Framework
Built on top of our robust blockchain network, the Cifer Machine Learning Framework provides the tools and infrastructure necessary for developing advanced AI models while ensuring data privacy and security. Our framework encompasses several cutting-edge techniques and is designed for large-scale expansion, allowing for future enhancements and the integration of additional methodologies. Currently, we offer five key frameworks:
Federated Learning:
Description: Enables multiple parties to collaboratively train AI models without sharing raw data.
Benefit: Ensures data privacy by keeping sensitive information on local devices.
Swarm Intelligence:
Description: Utilizes collective behavior principles to solve complex problems through decentralized agent cooperation.
Benefit: Enhances the robustness and efficiency of AI models through distributed learning and decision-making.
Fully Homomorphic Encryption (FHE):
Description: Allows computations to be performed on encrypted data without needing to decrypt it first.
Benefit: Maintains data privacy throughout the entire computational process, ensuring end-to-end security.
Multi-Party Computation (MPC):
Description: Facilitates secure computations involving multiple parties, where each party’s input remains private.
Benefit: Enables collaborative computation without compromising the confidentiality of individual data inputs.
Multi-Agent Systems:
Description: Involves multiple interacting agents that can learn, adapt, and collaborate to achieve specific goals.
Benefit: Improves the scalability and adaptability of AI models by leveraging the interactions and cooperation of multiple agents.