This session will present and seek feedback on a novel decentralized reference and access computing infrastructure for archives and digital humanities researchers called ClioX. ClioX explores the research question: "How can Privacy Enhancing Technologies (PETS) (e.g., compute-to-data design, data spaces, privacy-preserving federated machine learning, securemultiparty computation, distant reading and visualization) be used to provide greater access to sensitive archival documents without compromising privacy?" ClioX was developed because Archives face mounting archival documents of a sensitive nature and growing researcher requests for access and analysis using AI. Archives struggle to conduct sensitivity reviews using manual or automated techniques. ClioX solves this problem by: (1) allowing archives to make archival datasets available to researchers to run a variety of AI-enabled computations (e.g., Exploratory Data Analysis, Clustering, Topic Modelling, and SentimentAnalysis) and (2) allowing researchers to access one or many archival datasets to run AI algorithms over archival datasets in archival 'data spaces' so that the data never leaves the custody and control of the archives but is still able to return aggregated output results to researchers. ClioX builds upon the EU's PontusX, open source framework for the industrial AI and data economy and the largest publicly available X-Ecosystem, which is powered by smart contracts and Gaia-X and transforms EU Data Act compliance into scalable collaboration and monetization opportunities that have the potential not only to provide greater access to archival data but also to create new business models for archives and digital humanities that leverage the power of decentralized governance and computing.