Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Processors

[2606.09402] Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Processors Skip to main content Search Submit Donate Log in Computer Science > Cryptography and Security arXiv:2606.09402 (cs) [Submitted on 8 Jun 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Processors Authors: Takao Murakami , Yuichi Sei , Reo Eriguchi View PDF HTML (experimental) Abstract: In the shuffle model of DP (Differential Privacy), a shuffler randomly permutes users' data to achieve high accuracy and privacy. Recent studies show that most existing shuffle protocols are vulnerable to collusion attacks by the data collector and users. They address this issue by introducing the augmented shuffle model that incorporates random sampling and dummy data addition into the shuffler. However, it remains open how to ensure the shuffler follows the protocol and does not collude with the data collector in this model. We address this trust issue by thoroughly exploring the augmented shuffle model with TEEs (Trusted Execution Environments). We first introduce a new privacy notion, FODP (Fully Oblivious DP), which strengthens DP to prevent various TEE side-channel attacks based on external/internal memory access patterns and control flows. We propose a general framework for FODP algorithms based on memory-size obfuscation and three concrete algorithms within it. We also improve the efficiency of our algorithms by using the count-min sketch and optimizing the number of hashes. We evaluate our algorithms on Intel SGX and demonstrate their effectiveness through comparisons with nine baselines. Comments: Full version of the paper accepted at USENIX Security 2026; The current version added an addendum to the runtime (Appendix F) Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.09402 [cs.CR] (or arXiv:2606.09402v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2606.09402 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Takao Murakami [ view email ] [v1] Mon, 8 Jun 2026 12:21:42 UTC (3,132 KB) [v2] Thu, 27 Aug 2026 02:22:02 UTC (3,355 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CR < prev | next > new | recent | 2026-06 Change to browse by: cs References & Citations SciX 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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