Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence

Computer Science > Cryptography and Security [Submitted on 22 May 2026] Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence Yannik Dittmar, Marvin Jerome Stephan, Thomas Völkl, Matthias Hollick, Jiska Classen Many existing Artificial Intelligence (AI) solutions on mobile devices rely on an extensive collection of sensitive data, raising privacy concerns and often requiring storage for both context and model improvement. Apple's Private Cloud Compute (PCC) aims to address this by emphasizing mobile device integration and a privacy-first design. The central claim of PCC is that it does not store any user data and that user input and user accounts are unlinkable. While most of the PCC system specifications are public, compiled binaries add a layer of opaqueness. There are no reproducible builds, and there are no symbols within those binaries, creating potential discrepancies between the specification and what is shipped to the user. Additionally, the underlying models and interfaces for querying PCC are not openly accessible, limiting academic evaluation of model properties, such as accuracy. This poses a challenge in assessing whether a privacy-preserving approach like PCC is actually trustworthy while also providing high-quality answers. We are the first to reverse-engineer the PCC implementation on mobile devices to evaluate privacy aspects and to open its non-public interfaces on local devices to support custom PCC queries. We demonstrate this level of access beyond Apple's intended use cases by independently benchmarking the PCC model. We enable future research by making our PCC benchmarking framework publicly available. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2605.24239 [cs.CR]   (or arXiv:2605.24239v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.24239 Focus to learn more Journal reference: Proceedings of the 19th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec 2026) Related DOI: https://doi.org/10.1145/3765613.3811691 Focus to learn more Submission history From: Jiska Classen [view email] [v1] Fri, 22 May 2026 21:31:17 UTC (529 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)