Security Architecture
Trust Nothing.
Verify Everything.
Sangrah implements defense-in-depth across every layer of the federated learning pipeline. Here's how we protect your data sovereignty.
01 / Mutual TLS
End-to-End mTLS Authentication
Every connection between participant nodes and the coordinator is authenticated via mutual TLS with X.509 certificates. No anonymous traffic enters the federation.
# Certificate verification sangrah cert verify --node enterprise-a Issuer: Sangrah Federation CA Subject: CN=enterprise-a.sangrah.net Valid: 2025-01-01 to 2026-01-01 Status: ✓ Valid
02 / Model Signing
Ed25519 Model Integrity
Every model artifact is signed with Ed25519 before distribution. Participants verify signatures before accepting model updates, preventing supply-chain attacks on the learning pipeline.
# Model signature verification sangrah model verify --version 1.2.4 Algorithm: Ed25519 Signer: coordinator@sangrah.net Hash: sha256:a4f2c8... Status: ✓ Signature valid
03 / Differential Privacy
Formal Privacy Guarantees
Configurable (ε, δ)-differential privacy ensures that individual data points cannot be reverse-engineered from shared gradients. Privacy budgets are tracked per-epoch with automatic training halts when thresholds are reached.
# Privacy budget status sangrah privacy status Epsilon (ε): 3.2 / 10.0 Delta (δ): 1e-5 Mechanism: Gaussian Epochs used: 24 / 50 Status: ✓ Within budget
04 / Secure Aggregation
Pairwise Masking Protocol
Gradient updates are protected using a pairwise masking scheme. The coordinator aggregates masked gradients without ever seeing individual contributions — even a compromised coordinator learns nothing about any single participant.
# Aggregation verification sangrah aggregate verify --epoch 24 Protocol: SecAgg (pairwise) Participants: 5/5 masks verified Dropped: 0 (threshold: 2) Status: ✓ Secure aggregation complete
05 / Byzantine Resilience
Multi-Krum Defense
The Multi-Krum algorithm detects and excludes Byzantine (adversarial or faulty) gradient updates. Malicious participants cannot poison the global model — the aggregation is provably robust to a configurable fraction of corrupted nodes.
# Byzantine detection log
sangrah audit byzantine --epoch 24
Algorithm: Multi-Krum (f=1)
Scores:
enterprise-a: 0.12 ✓
enterprise-b: 0.15 ✓
enterprise-c: 0.14 ✓
enterprise-d: 3.82 ✗ EXCLUDED
Status: 1 node excluded06 / Zero Trust
Zero-Trust Architecture
No implicit trust between any components. Every API call is authenticated, every model artifact is verified, every gradient is validated. The system assumes breach at every layer and verifies accordingly.
# Security posture sangrah security audit mTLS: ✓ Enforced Model signing: ✓ Ed25519 Privacy: ✓ ε=3.2 (budget ok) SecAgg: ✓ Pairwise masking Byzantine: ✓ Multi-Krum active Cert rotation: ✓ 30-day cycle Overall: HARDENED