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Open access

Tarik Moataz, Erik-Oliver Blass and Guevara Noubir

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.

Open access

Erik-Oliver Blass, Travis Mayberry and Guevara Noubir

Abstract

We revisit the problem of privacy-preserving range search and sort queries on encrypted data in the face of an untrusted data store. Our new protocol RASP has several advantages over existing work. First, RASP strengthens privacy by ensuring forward security: after a query for range [a, b], any new record added to the data store is indistinguishable from random, even if the new record falls within range [a, b]. We are able to accomplish this using only traditional hash and block cipher operations, abstaining from expensive asymmetric cryptography and bilinear pairings. Consequently, RASP is highly practical, even for large database sizes. Additionally, we require only cloud storage and not a computational cloud like related works, which can reduce monetary costs significantly. At the heart of RASP, we develop a new update-oblivious bucket-based data structure. We allow for data to be added to buckets without leaking into which bucket it has been added. As long as a bucket is not explicitly queried, the data store does not learn anything about bucket contents. Furthermore, no information is leaked about data additions following a query. Besides formally proving RASP’s privacy, we also present a practical evaluation of RASP on Amazon Dynamo, demonstrating its efficiency and real world applicability.