LLM Privacy-Preserving Computation
LLM-Berechnungen ohne Privacy-Preserving können sensible Daten offenlegen — ohne Datenschutztechniken bleibt Privatsphäre ungeschützt. Vier Kontrollen: Federated Learning, Differential Privacy, SMPC und Homomorphic Encryption.
Was ist LLM Privacy-Preserving Computation? Einfach erklärt
LLM Privacy-Preserving Computation schützt sensible Daten bei LLM-Berechnungen: Federated Learning trainiert auf verteilten Daten ohne Zentralisierung. Differential Privacy fügt calibrated Noise zu Outputs oder Gradients um Re-Identification zu verhindern. Secure Multi-Party Computation (SMPC) berechnet auf verschlüsselten Daten über mehrere Parteien ohne individuelle Inputs zu offenbaren. Homomorphic Encryption ermöglicht Berechnungen auf verschlüsselten Daten ohne Entschlüsselung. Ohne Privacy-Preserving können sensible Daten bei Training und Inference offenlegt werden.
↓ Springe zu Privacy-Preserving-Kontrollen
4 Privacy-Preserving-Computation-Kontrollen
Train LLMs on distributed data without centralising data. Data stays on local devices, only model updates are shared.
# Moltbot federated learning:
federated_learning:
enabled: true
# Federated learning architecture:
architecture:
# Central server coordinates training
# Edge devices train on local data
# Model updates aggregated centrally
# Data never leaves local devices
# Aggregation method:
aggregation:
# Use: Federated Averaging (FedAvg)
# Aggregates: model weights from edge devices
# Weighted: by number of samples per device
# Secure: encrypted communication for updates
# Privacy guarantees:
privacy:
# Data: remains on local devices
# Updates: only model gradients shared
# Differential privacy: add noise to gradients
# Minimum clients: required for aggregationAdd calibrated noise to LLM outputs to protect individual privacy. Use differential privacy to prevent re-identification.
# Moltbot differential privacy:
differential_privacy:
enabled: true
# Privacy budget:
privacy_budget:
# Epsilon: privacy parameter
# Lower epsilon = stronger privacy
# Typical: epsilon = 1.0 to 10.0
epsilon: 1.0
# Noise mechanism:
noise:
# Use: Gaussian mechanism for continuous data
# Or: Laplace mechanism for discrete data
# Add: noise to model outputs or gradients
# Calibrate: based on sensitivity
# Privacy tracking:
tracking:
# Track: privacy budget consumption
# Alert: when budget exhausted
# Reset: budget periodically
# Audit: privacy budget usageCompute on encrypted data across multiple parties without revealing individual inputs. Use SMPC for collaborative LLM training.
# Moltbot secure multi-party computation:
smpc:
enabled: true
# SMPC protocol:
protocol:
# Use: Yao's garbled circuits or secret sharing
# Parties: 2 or more parties
# Compute: on encrypted inputs
# Reveal: only final result
# Secret sharing:
secret_sharing:
# Split: input into shares
# Distribute: shares to parties
# Compute: on shares without revealing input
# Reconstruct: result from shares
# Security guarantees:
security:
# Privacy: inputs remain private
# Correctness: result is correct
# Fairness: all parties receive result
# Verifiability: result can be verifiedCompute on encrypted data without decryption. Use homomorphic encryption for privacy-preserving LLM inference.
# Moltbot homomorphic encryption:
homomorphic_encryption:
enabled: true
# Encryption scheme:
scheme:
# Use: Fully Homomorphic Encryption (FHE)
# Or: Partially Homomorphic Encryption (PHE)
# FHE: supports arbitrary computations
# PHE: supports limited operations (addition or multiplication)
# Inference on encrypted data:
inference:
# Encrypt: input data
# Compute: on encrypted data
# Decrypt: only output
# Privacy: input data never revealed
# Performance considerations:
performance:
# FHE: computationally expensive
# PHE: faster but limited operations
# Hardware: use FHE-accelerated hardware
# Optimisation: batch processingHäufige Fragen
What is the difference between federated learning and differential privacy?
Federated learning is a training paradigm where data stays on local devices and only model updates are shared. It addresses data centralisation by training on distributed data. Differential privacy is a technique that adds calibrated noise to data or model outputs to protect individual privacy. It addresses re-identification by making it difficult to determine whether a specific individual's data was used. Both are often used together: federated learning keeps data local, differential privacy adds noise to model updates to prevent privacy leaks. Federated learning is about where computation happens. Differential privacy is about how privacy is mathematically guaranteed.
How does secure multi-party computation (SMPC) work?
SMPC allows multiple parties to compute a function on their combined inputs without revealing individual inputs. Each party encrypts their input using secret sharing or garbled circuits. The computation is performed on the encrypted inputs, and only the final result is revealed. No party learns anything about other parties' inputs. Example: Three parties want to compute the average of their salaries without revealing individual salaries. Using SMPC, each party shares encrypted salary data, the average is computed on encrypted data, and only the average is revealed. SMPC is computationally expensive but provides strong privacy guarantees.
What are the performance implications of homomorphic encryption?
Homomorphic encryption allows computation on encrypted data, but it is computationally expensive. Fully Homomorphic Encryption (FHE) supports arbitrary computations but is 100-1000x slower than plaintext computation. Partially Homomorphic Encryption (PHE) is faster (10-100x slower) but supports only limited operations (addition or multiplication, not both). Optimisation strategies: 1) Use FHE-accelerated hardware (GPUs, ASICs). 2) Batch operations to amortise overhead. 3) Use PHE when possible (e.g., only need addition). 4) Pre-compute common operations. 5) Use hybrid approaches (partial decryption for intermediate steps).
When should I use privacy-preserving techniques for LLMs?
Privacy-preserving techniques are necessary when: 1) Data is sensitive (PII, health data, financial data). 2) Data cannot be centralised (regulatory constraints, data sovereignty). 3) Collaboration is required across multiple parties (multi-party training). 4) Privacy guarantees are required (GDPR, HIPAA). 5) Risk of re-identification is high. Federated learning is suitable for distributed training. Differential privacy is suitable for protecting individual contributions. SMPC is suitable for collaborative computation. Homomorphic encryption is suitable for privacy-preserving inference. Use the technique that matches your use case and constraints.