Cloak: Heuristic ORAM Optimization Through Fixed Temporal Distribution

--> Computer Science > Cryptography and Security arXiv:2605.27565 (cs) [Submitted on 26 May 2026] Title: Cloak: Heuristic ORAM Optimization Through Fixed Temporal Distribution Authors: Onur Eren Arpaci , Florian Kerschbaum , Sujaya Maiyya View a PDF of the paper titled Cloak: Heuristic ORAM Optimization Through Fixed Temporal Distribution, by Onur Eren Arpaci and 2 other authors View PDF HTML (experimental) Abstract: Encrypted cloud storage can hide data contents but still leak sensitive information through access patterns. ORAM addresses this by hiding access patterns, but existing ORAM systems are too inefficient to deploy in practice. We present Cloak, an oblivious storage system that dramatically improves performance by leveraging a simple, widely observed property of real workloads: temporal locality, where recently accessed items are more likely to be accessed again soon. Instead of trying to make server accesses look perfectly uniform, Cloak makes server traffic follow a fixed, "recentness-biased" pattern and then uses real queries to fill as much of that traffic as possible. When the workload exhibits temporal locality, Cloak achieves overheads as low as $1.1\times$ over a non-oblivious and unencrypted baseline. Importantly, this heuristic affects only performance, not security. We evaluate Cloak on Netflix click-stream and Ethereum transaction traces, achieving 165,000 and 157,000 operations per second, respectively, on a single machine. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.27565 [cs.CR] (or arXiv:2605.27565v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2605.27565 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Onur Eren Arpaci [ view email ] [v1] Tue, 26 May 2026 18:36:49 UTC (522 KB) Full-text links: Access Paper: View a PDF of the paper titled Cloak: Heuristic ORAM Optimization Through Fixed Temporal Distribution, by Onur Eren Arpaci and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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? )