IRO: Integrity and Reliability Enhanced Ring ORAM

12/28/2020
by   Wenpeng He, et al.
0

Memory security and reliability are two of the major design concerns in cloud computing systems. State-of-the-art memory security-reliability co-designs (e.g. Synergy) have achieved a good balance on performance, confidentiality, integrity, and reliability. However, these works merely rely on encryption to ensure data confidentiality, which has been proven unable to prevent information leakage from memory access patterns. Ring ORAM is an attractive confidential protection protocol to hide memory access patterns to the untrusted storage system. Unfortunately, it does not compatible with the security-reliability co-designs. A forced combination would result in more severe performance loss. In this paper, we propose IRO, an Integrity and Reliability enhanced Ring ORAM design. To reduce the overhead of integrity verification, we propose a low overhead integrity tree RIT and use a Minimum Update Subtree Tree (MUST) to reduce metadata update overhead. To improve memory reliability, we present Secure Replication to provide channel-level error resilience for the ORAM tree and use the mirrored channel technique to guarantee the reliability of the MUST. Last, we use the error correction pointer (ECP) to repair permanent memory cell fault to further improve device reliability and lifetime. A compact metadata design is used to reduce the storage and consulting overhead of the ECP. IRO provides strong security and reliability guarantees, while the resulting storage and performance overhead is very small. Our evaluation shows that IRO only increases 7.54 channels four AES-GCM units setting. With enough AES-GCM units to perform concurrent MAC computing, IRO can reduce 2.14

READ FULL TEXT

page 1

page 10

page 11

page 12

research
03/10/2020

Streamlining Integrity Tree Updates for Secure Persistent Non-Volatile Memory

Emerging non-volatile main memory (NVMM) is rapidly being integrated int...
research
08/26/2020

GuardNN: Secure DNN Accelerator for Privacy-Preserving Deep Learning

This paper proposes GuardNN, a secure deep neural network (DNN) accelera...
research
09/01/2022

SecDDR: Enabling Low-Cost Secure Memories by Protecting the DDR Interface

The security goals of cloud providers and users include memory confident...
research
12/10/2019

A Write-Friendly and Fast-Recovery Scheme for Security Metadata in NVM

Non-Volatile Memories (NVMs) have attracted the attentions of academia a...
research
10/18/2019

n-m-Variant Systems: Adversarial-Resistant Software Rejuvenation for Cloud-Based Web Applications

Web servers are a popular target for adversaries as they are publicly ac...
research
07/13/2018

Improving 3D NAND Flash Memory Lifetime by Tolerating Early Retention Loss and Process Variation

Compared to planar (i.e., two-dimensional) NAND flash memory, 3D NAND fl...
research
04/19/2022

Seculator: A Fast and Secure Neural Processing Unit

Securing deep neural networks (DNNs) is a problem of significant interes...

Please sign up or login with your details

Forgot password? Click here to reset