Mingshen Sun and Mateus Guzzo, TikTok
AI has transformed how people learn, work and live - automating complex tasks and extracting insight from massive datasets. But most powerful AI today (especially large language models) runs on server-class hardware, which typically means user prompts and context must be visible to the service provider to be processed. While acceptable for some cases, it is still challenging with highly sensitive data where users expect similar protections as end-to-end encryption. Private Verifiable Compute (PVC) is a technical solution that can enable users to initiate a request to a private and verifiable environment for context-aware AI processing with sensitive data, where no one, including service providers, can access them. With PVC in the cloud environment, it unleashes full potentials of AI hardware in the data center for complex AI tasks, such as large language models (LLMs), generative AI and beyond, while guaranteeing user privacy and verifiable transparency.

Mingshen Sun is a research scientist at TikTok, leading innovation and adoption of the Privacy Enhancing Technologies and Confidential Computing. Previously, he worked on multiple open source projects on building safe, secure and trustworthy systems. Mingshen also published academic papers and presented industry innovations on topics at the intersection of privacy and security, operating systems, and programming languages. He also serves on Technical Advisory Council of Confidential Computing Consortium.

Mateus Guzzo is a community architect, researcher, and designer at the intersection of privacy, open technology, and platform governance. He is a community & developer advocate for privacy enhancing technologies (PETs) at TikTok, a member of the Confidential Computing Consortium Outreach Committee, and an advisory board member for OpenUK's SooCon26. His work is focused on co-designing platforms for responsible technology cooperation.

author = {Mingshen Sun and Mateus Guzzo},
title = {Private {AI}: Building Trust Through Verifiable Computation},
year = {2026},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = jun
}
