Neusha Javidnia

We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLM owners by embedding unique and verifiable signatures in the generated output. Existing approaches rely on manually crafted transformation rules to preserve watermarked code functionality or manipulate token-generation probabilities at inference time, which are prone to compilation errors. To address these challenges, SWaRL employs a reinforcement learning-based co-training framework that uses compiler feedback for functional correctness and a jointly trained confidential verifier as a reward signal to maintain watermark detectability. Furthermore, SWaRL employs low-rank adaptation (LoRA) during fine-tuning, allowing the learned watermark information to be transferable across model updates. Extensive experiments show that SWaRL achieves higher watermark detection accuracy compared to prior methods while fully maintaining watermarked code functionality. The LoRA-based signature embedding steers the base model to generate and solve code in a watermark-specific manner without significant computational overhead. Moreover, SWaRL exhibits strong resilience against refactoring and adversarial transformation attacks.

The widespread deployment of cloud-hosted generative models raises a fundamental challenge: enabling efficient autoregressive generation while preserving the privacy of both user prompts and model parameters in untrusted environments. We address this challenge in a client-server setting where an untrusted server hosts an autoregressive Transformer and the client requires cryptographic protection for both inputs and inference. We present CryptoGen, the first system to enable scalable privacy-preserving neural generation with persistent encrypted key-value (KV) cache reuse. Discriminative-task secure inference systems incur quadratic latency and memory growth when adapted to autoregressive decoding due to the lack of native encrypted KV-cache support. In contrast, CryptoGen achieves near-linear scaling by securely reusing and updating encrypted KV caches throughout generation. CryptoGen integrates homomorphic encryption and secret sharing to support both prefilling and generation. Key techniques include a unified encrypted KV-cache framework, heterogeneous SIMD encodings for different phases, optimized cipher-cipher matrix-matrix and matrix-vector operations, and efficient noise refresh and ciphertext concatenation mechanisms. Evaluation on generative Transformer models trained on WikiText-2, PTB, and LAMBADA shows that for input lengths of 128-512 tokens, CryptoGen achieves 4.4x-7.6x lower per-token latency than state-of-the-art discriminative secure inference systems, while maintaining near-linear latency and memory scaling, with advantages increasing for longer sequences. CryptoGen is released as an open-source library.