Oblivious RAM

Author:

Chang Zhao1,Xie Dong1,Li Feifei1

Affiliation:

1. University of Utah

Abstract

Many companies choose the cloud as their data and IT infrastructure platform. The remote access of the data brings the issue of trust. Despite the use of strong encryption schemes, adversaries can still learn valuable information regarding encrypted data by observing the data access patterns. To that end, one can hide the access patterns, which may leak sensitive information, using Oblivious RAMs (ORAMs). Numerous works have proposed different ORAM constructions, but they have never been thoroughly compared against and tested on large databases. There are also no open source implementation of these schemes. These limitations make it difficult for researchers and practitioners to choose and adopt a suitable ORAM for their applications. To address this issue, we provide a thorough study over several practical ORAM constructions, and implement them under the same library. We perform extensive experiments to provide insights into their performance characteristics with respect to efficiency, scalability, and communication cost.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Single Round-trip Hierarchical ORAM via Succinct Indices;Proceedings of the 19th ACM Asia Conference on Computer and Communications Security;2024-07

2. Caching and Prefetching for Improving ORAM Performance;2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W);2024-06-24

3. Secure and Practical Functional Dependency Discovery in Outsourced Databases;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. Parameter-Hiding Order-Revealing Encryption Without Pairings;Lecture Notes in Computer Science;2024

5. Ensuring End-to-End IoT Data Security and Privacy Through Cloud-Enhanced Confidential Computing;Lecture Notes in Computer Science;2024

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