DPSN: Decentralised Pub/Sub Network
  • Introduction
  • Why Decentralized?
  • Understanding Topics
  • Architecture
    • Topics Registry
    • Configurator
    • Clusters
    • Publishers and Subscribers
    • Facilitator Brokers
    • SDK
  • Functionality
    • Message Publishing and Delivery
    • Subscription Management
    • Security Considerations
    • Topic Ownership and Access Control
    • Private Key Authentication
    • Fully Homomorphic Encryption Support
  • Advantages and Use Cases
    • Advantages of DPSN
    • Use Cases
  • Integration
    • SDK Introduction
    • Publisher
    • Subscriber
    • Delegated Addresses
    • Private Messaging
  • Integration Guides
    • Messaging Application
  • Token Use
    • Utility
    • Token Utility Model
  • DIPs
    • DIP1: Stateless Message Routing in DPSN
  • DIP2: Integration of DPSN with Model Context Protocol (MCP)
  • DIP-3: Standardizing DPSN AVS for Enhanced Security and Reliability
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  1. Functionality

Fully Homomorphic Encryption Support

DPSN incorporates fully homomorphic encryption (FHE) as a cornerstone of its privacy-preserving architecture. FHE empowers users to perform computations on encrypted data without compromising its confidentiality. This section delves into the technical details and benefits of FHE within the DPSN ecosystem.

FHE Integration

DPSN seamlessly integrates FHE into its core functionalities, allowing users to encrypt data before publication and perform computations on encrypted data without compromising privacy. The FHE scheme is embedded within the DPSN SDKs, providing a transparent and user-friendly experience.

Encryption and Decryption

  • Data Encryption: Users can encrypt data using FHE keys before publishing it on the DPSN network.

  • Homomorphic Operations: The network supports scalable operations on encrypted data.

  • Decryption: Only authorized parties possessing the decryption key can decrypt the final result of computations.

Security and Privacy

  • Key Management: DPSN employs robust key management practices to protect FHE keys and prevent unauthorized access.

  • Homomorphic Error: The network mitigates the impact of homomorphic error through error correction techniques and parameter optimization.

  • Privacy Preservation: FHE ensures that no sensitive information is exposed during data transmission or processing.

Performance Optimization

  • Hardware Acceleration: DPSN custom facilitators provides specialized hardware accelerators to optimize FHE computations, reducing latency and improving overall performance.

  • Approximation Techniques: Employing approximation techniques to balance accuracy and computational efficiency.

  • Parallel Processing: Distributing FHE computations across multiple nodes to enhance performance.

Use Cases

  • Secure Data Analytics: FHE enables powerful data analytics without compromising data privacy.

  • Private Machine Learning: Training and deploying machine learning models on encrypted data.

  • Secure Supply Chain: Protecting sensitive product information and supply chain data.

  • Financial Services: Enabling secure and private financial transactions and analysis.

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Last updated 8 months ago