3 papers
cs.CR2026
Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs
Arka Pal, Louai Zahran, William Gvozdjak +2
As large language models (LLMs) continue to grow in size, fewer users are able to host and run models locally. This has led to increased use of third-party hosting services. Howeve…
cs.LG2025
Cascade: Token-Sharded Private LLM Inference
Rahul Thomas, Louai Zahran, Erica Choi +3
As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where…
cs.CR2025
An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs
Rahul Thomas, Louai Zahran, Erica Choi +3
Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of perfor…