Swarm: Decentralized Cloud for AI
  • Introduction
    • The Problem
    • How Swarm works
    • Built for AGI
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  • Key Benefits
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    • Confidential Computing: Secure Enclave Architecture
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  • Technical Specifications
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  • Future Developments
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    • Strategic Objectives and Collaboration
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    • Market Expansion Framework
    • Expansion Targets: Strategic Growth Objectives
    • Integration Architecture: Technology Integration Framework
    • Integration Roadmap: Phased Approach to Technology Integration
  • Reward System Architecture: Network Incentives and Rewards
    • Reward Framework
    • Reward Distribution Matrix: Metrics and Weighting for Equitable Rewards
    • Hardware Provider Incentives: Performance-Based Rewards Framework
    • Dynamic Reward Scaling: Adaptive Incentive Framework
    • Resource Valuation Factors: Dynamic Adjustment Model
    • Network Growth Incentives: Expansion Rewards Framework
    • Long-term Incentive Structure: Rewarding Sustained Contributions
    • Performance Requirements: Metrics and Impact on Rewards
    • Sustainability Mechanisms: Ensuring Economic Balance
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    • Innovation Incentives: Driving Technological Advancement and Network Growth
  • Network Security and Staking
    • Staking Architecture
    • Stake Requirements: Ensuring Commitment and Security
    • Security Framework: Network Protection Mechanisms
    • Security Components: Key Functions and Implementation
    • Monitoring Architecture: Real-Time Performance and Security Oversight
    • Monitoring Metrics: Key Service Indicators for Swarm
    • Risk Framework: Comprehensive Risk Management for Swarm
    • Risk Mitigation Strategies: Proactive and Responsive Measures
    • Slashing Conditions: Penalty Framework for Ensuring Accountability
    • Slashing Matrix: Violation Impact and Recovery Path
    • Network Protection: Comprehensive Security Architecture
    • Security Features: Robust Mechanisms for Network Integrity
    • Recovery Framework: Ensuring Resilience and Service Continuity
    • Recovery Process: Staged Actions for Incident Management
    • Security Governance: Integrated Oversight Framework
    • Control Framework: A Comprehensive Approach to Network Governance and Security
  • FAQ
    • How Swarm Parallelizes and Connects All GPUs
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  1. Privacy and Security

Data Privacy Framework

PreviousNetwork Security FeatureNextPrivacy Control

Last updated 4 months ago

Data Privacy Framework

Swarm's Data Privacy Framework ensures comprehensive protection and compliance for sensitive data throughout its lifecycle. The framework integrates privacy-by-design principles into its architecture, focusing on classification, enforcement, and monitoring.

Core Components and Functions

  1. Data Classification:

    • Purpose: Categorize data based on sensitivity and regulatory requirements.

    • Implementation:

      • Automated Scanning: Uses AI-powered tools to identify and classify sensitive data across the platform.

      • Policy Assignment: Dynamically assigns appropriate privacy policies based on classification results.

  2. Privacy Enforcement:

    • Purpose: Enforce privacy policies to safeguard classified data.

    • Implementation:

      • Access Control: Employs Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) to restrict data access to authorized users and processes.

      • Encryption: Ensures that all sensitive data is encrypted:

        • At-Rest: Secured with AES-256-GCM encryption.

        • In-Transit: Protected using TLS 1.3 within the Mesh VPN.

        • In-Use: Shielded with secure enclaves (Intel SGX/AMD SEV).

  3. Privacy Monitoring:

    • Purpose: Continuously track and validate adherence to privacy policies and detect violations.

    • Implementation:

      • Audit Logging: Maintains detailed logs of data access and usage for traceability and forensic analysis.

      • Compliance Checking: Automated checks ensure adherence to data privacy regulations (e.g., GDPR, CCPA).

      • Real-Time Monitoring: Detects anomalies or unauthorized access attempts to sensitive data.

Key Benefits

  • Data Protection: Ensures confidentiality, integrity, and availability of sensitive data across all states (in-transit, at-rest, and in-use).

  • Regulatory Compliance: Streamlines adherence to global privacy regulations with automated classification and compliance checking.

  • Transparency and Accountability: Audit logging and monitoring provide visibility into data handling practices, fostering trust with users and stakeholders.

  • Scalability: The framework dynamically adapts to diverse workloads and evolving privacy requirements.

Swarm’s Privacy Architecture delivers a robust, automated, and compliant solution for data privacy, empowering organizations to manage sensitive workloads securely and responsibly.