# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 3 Proposed Methods: The SecureLLM Framework
### Layer 1: Lightweight Cryptography  
To minimize the overhead, we adopt a hybrid encryption approach that uses symmetric key encryption (e.g., AES-GCM) for bulk data and asymmetric cryptography (e.g., Elliptic Curve Cryptography) for key exchange. We implement AES-GCM with a 256-bit key and utilize Curve25519 for the ECC key exchange, ensuring both robust encryption and efficient key management throughout the training process. When we describe this layer as \"lightweight\", we refer to the fact that typical encryption and decryption operations at the batch-level scale add only a small fraction of extra processing time (in the order of milliseconds or less per update) and require minimal additional memory (often a few megabytes at most), relative to the massive parameter storage of modern large language models. For enhanced security, ephemeral keys are generated for each training round using an ECDH protocol. These keys are securely distributed and are immediately destroyed after use to prevent replay attacks. During training, we encrypt mini-batches of gradients, ensuring that the data remain confidential even if an attacker gains partial access to the training pipeline. In contrast to conventional schemes that rely solely on homomorphic encryption, which can be prohibitively costly at scale, our method balances high-speed symmetric encryption for continuous data transfer with robust asymmetric primitives for secure key distribution. This \"lightweight cryptography\" specifically designates cryptographic methods that prioritize a reduced runtime overhead and manageable memory footprints while maintaining strong security guarantees, making them viable for large-scale distributed training [36]. Figure 1 presents the architecture of the Secure LLM Framework.  
Adopting this hybrid cryptographic method offers several benefits. First, it minimizes the latency in large-scale gradient exchanges, which is crucial when collaborating across multiple data centers or organizational units. Second, frequent key rotation becomes more manageable because the asymmetric layer only needs to handle the distribution of new session keys rather than encrypting entire gradients. Third, such a setup can be integrated with emerging post-quantum cryptographic primitives if organizations foresee long-term risks being posed by quantum-capable adversaries [37]. By designing Secure LLM's cryptographic layer in a modular way, one can swap out older algorithms (e.g., RSA) with elliptic curve or post-quantum techniques without substantial changes to the rest of the framework.  
Moreover, this layer can integrate with both the decentralized fine-tuning and differential privacy layers, ensuring consistent security protocols. For instance, if a gradient  
Figure 1: Secure LLM framework architecture.  
aggregator detects anomalous behavior or a suspicious update rate, it can trigger a forced key rotation to mitigate potential key compromise. All such interventions are facilitated through standardized APIs for key management and encryption, allowing the upper layers to remain abstracted from the underlying cryptographic specifics.  
A critical aspect of the cryptography layer is key management. SecureLLM enforces ephemeral key rotation for each training round, ensuring that the compromised keys from one round do not grant attackers access to future gradient transmissions. This process may involve using an established key exchange protocol (e.g., ECDH or Curve25519) under a trusted certificate authority, and if desired, threshold cryptography can distribute partial keys among multiple nodes. Consequently, even an attacker intercepting network traffic cannot decrypt large portions of the training process, thereby reducing the risk of data leakage.