Single Round-trip Hierarchical ORAM via Succinct Indices

• URL: http://arxiv.org/abs/2208.07489v3
• Date: Thu, 13 Jun 2024 00:16:38 GMT
• Title: Single Round-trip Hierarchical ORAM via Succinct Indices
• Authors: William Holland, Olga Ohrimenko, Anthony Wirth,
• Abstract summary: Rank ORAM can retrieve data with a single round-trip of communication. A emphcompressed client-side data structure stores, implicitly, the location of each element at the server.
• Score: 5.437298646956505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Access patterns to data stored remotely create a side channel that is known to leak information even if the content of the data is encrypted. To protect against access pattern leakage, Oblivious RAM is a cryptographic primitive that obscures the (actual) access trace at the expense of additional access and periodic shuffling of the server's contents. A class of ORAM solutions, known as Hierarchical ORAM, has achieved theoretically \emph{optimal} logarithmic bandwidth overhead. However, to date, Hierarchical ORAMs are seen as only theoretical artifacts. This is because they require a large number of communication round-trips to locate (shuffled) elements at the server and involve complex building blocks such as cuckoo hash tables. To address the limitations of Hierarchical ORAM schemes in practice, we introduce Rank ORAM; the first Hierarchical ORAM that can retrieve data with a single round-trip of communication (as compared to a logarithmic number in previous work). To support non-interactive communication, we introduce a \emph{compressed} client-side data structure that stores, implicitly, the location of each element at the server. In addition, this location metadata enables a simple protocol design that dispenses with the need for complex cuckoo hash tables. Rank ORAM requires asymptotically smaller memory than existing (non-Hierarchical) state-of-the-art practical ORAM schemes (e.g., Ring ORAM) while maintaining comparable bandwidth performance. Our experiments on real network file-system traces demonstrate a reduction in client memory, against existing approaches, of a factor of~$100$. For example, when {outsourcing} a database of $17.5$TB, required client-memory is only $290$MB vs. $40$GB for standard approaches.

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