CryptoDB
NanoGRAM: Garbled RAM with $\widetilde{O}(\log N)$ Overhead
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| Conference: | EUROCRYPT 2023 |
| Abstract: | We propose a new garbled RAM construction called NanoGRAM, which achieves an amortized cost of $\widetilde{O}(\lambda \cdot (W \log N + \log^3 N))$ bits per memory access, where $\lambda$ is the security parameter, $W$ is the block size, and $N$ is the total number of blocks, and $\widetilde{O}(\cdot)$ hides $poly\log\log$ factors. For sufficiently large blocks where $W = \Omega(\log^2 N)$, our scheme achieves $\widetilde{O}(\lambda \cdot W \log N)$ cost per memory access, where the dependence on $N$ is optimal (barring $poly\log\log$ factors), in terms of the evaluator's runtime. Our asymptotical performance matches even the {\it interactive} state-of-the-art (modulo $poly\log\log$ factors), that is, running Circuit ORAM atop garbled circuit, and yet we remove the logarithmic number of interactions necessary in this baseline. Furthermore, we achieve asymptotical improvement over the recent work of Heath et al.~(Eurocrypt '22). Our scheme adopts the same assumptions as the mainstream literature on practical garbled circuits, i.e., circular correlation-robust hashes or a random oracle. We evaluate the concrete performance of NanoGRAM and compare it with a couple of baselines that are asymptotically less efficient. We show that NanoGRAM starts to outperform the na\"ive linear-scan garbled RAM at a memory size of $N = 2^9$ and starts to outperform the recent construction of Heath et al. at $N = 2^{13}$. Finally, as a by product, we also show the existence of a garbled RAM scheme assuming only one-way functions, with an amortized cost of $\widetilde{O}(\lambda^2 \cdot (W \log N + \log^3 N))$ per memory access. Again, the dependence on $N$ is nearly optimal for blocks of size $W = \Omega(\log^2 N)$ bits. |
BibTeX
@inproceedings{eurocrypt-2023-32859,
title={NanoGRAM: Garbled RAM with $\widetilde{O}(\log N)$ Overhead},
publisher={Springer-Verlag},
doi={10.1007/978-3-031-30545-0_16},
author={Andrew Park and Wei-Kai Lin and Elaine Shi},
year=2023
}