Privacy-Preserving Group Data Access via Stateless Oblivious RAM Simulation

TL;DR

This paper presents a privacy-preserving data access scheme combining probabilistic encryption and stateless oblivious RAM simulation, achieving low overhead and practical efficiency for group data access in outsourced storage.

Contribution

It introduces a novel hierarchy of cuckoo hash tables with a shared stash and demonstrates a scheme with $O( ext{log} n)$ amortized overhead, including experimental validation.

Findings

Achieves $O( ext{log} n)$ amortized overhead for privacy-preserving data access.

Experimental results show the scheme's practicality.

Eliminates pseudorandom hash functions at the cost of $O( ext{log}^2 n)$ overhead.

Abstract

We study the problem of providing privacy-preserving access to an outsourced honest-but-curious data repository for a group of trusted users. We show that such privacy-preserving data access is possible using a combination of probabilistic encryption, which directly hides data values, and stateless oblivious RAM simulation, which hides the pattern of data accesses. We give simulations that have only an $O(\log n)$ amortized time overhead for simulating a RAM algorithm, $\cal A$, that has a memory of size $n$, using a scheme that is data-oblivious with very high probability assuming the simulation has access to a private workspace of size $O(n^\nu)$, for any given fixed constant $\nu>0$. This simulation makes use of pseudorandom hash functions and is based on a novel hierarchy of cuckoo hash tables that all share a common stash. We also provide results from an experimental simulation of this scheme, showing its practicality. In addition, in a result that may be of some theoretical interest, we also show that one can eliminate the dependence on pseudorandom hash functions in our simulation while having the overhead rise to be $O(\log^2 n)$.