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Recursive Trees for Practical ORAM
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Abstract
We present a new, general data structure that reduces the communication cost of recent tree-based ORAMs. Contrary to ORAM trees with constant height and path lengths, our new construction r-ORAM allows for trees with varying shorter path length. Accessing an element in the ORAM tree results in different communication costs depending on the location of the element. The main idea behind r-ORAM is a recursive ORAM tree structure, where nodes in the tree are roots of other trees. While this approach results in a worst-case access cost (tree height) at most as any recent tree-based ORAM, we show that the average cost saving is around 35% for recent binary tree ORAMs. Besides reducing communication cost, r-ORAM also reduces storage overhead on the server by 4% to 20% depending on the ORAM’s client memory type. To prove r-ORAM’s soundness, we conduct a detailed overflow analysis. r-ORAM’s recursive approach is general in that it can be applied to all recent tree ORAMs, both constant and poly-log client memory ORAMs. Finally, we implement and benchmark r-ORAM in a practical setting to back up our theoretical claims.
Privacy Enhancing Technologies Symposium Advisory Board
Title: Recursive Trees for Practical ORAM
Description:
Abstract
We present a new, general data structure that reduces the communication cost of recent tree-based ORAMs.
Contrary to ORAM trees with constant height and path lengths, our new construction r-ORAM allows for trees with varying shorter path length.
Accessing an element in the ORAM tree results in different communication costs depending on the location of the element.
The main idea behind r-ORAM is a recursive ORAM tree structure, where nodes in the tree are roots of other trees.
While this approach results in a worst-case access cost (tree height) at most as any recent tree-based ORAM, we show that the average cost saving is around 35% for recent binary tree ORAMs.
Besides reducing communication cost, r-ORAM also reduces storage overhead on the server by 4% to 20% depending on the ORAM’s client memory type.
To prove r-ORAM’s soundness, we conduct a detailed overflow analysis.
r-ORAM’s recursive approach is general in that it can be applied to all recent tree ORAMs, both constant and poly-log client memory ORAMs.
Finally, we implement and benchmark r-ORAM in a practical setting to back up our theoretical claims.
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