The importance of hardware security increases significantly to protect the vast amounts of private data stored on edge devices. Physical unclonable functions (PUFs) are gaining prominence as hardware security primitives due to their ability to generate true random digital keys by exploiting the inherent randomness of the physical devices. Traditional approaches, however, require significant data movement between memory units and PUF generation circuits to perform encryption, presenting considerable energy efficiency and security challenges. This study introduces an innovative approach where PUF key generation and encryption are accomplished in the same vertically integrated resistive random access memory (V-RRAM), alleviating the data movement issue. The proposed V-RRAM encryption machine offers concealable PUFs, high area efficiency, and multi-thread data handling using parallel XOR logic operations. The encryption machine is compared with other machines, demonstrating the highest spatiotemporal cost-effectiveness.

Download full-text PDF

Source
http://dx.doi.org/10.1039/d4nh00420eDOI Listing

Publication Analysis

Top Keywords

encryption machine
12
physical unclonable
8
hardware security
8
data movement
8
encryption
5
concealable physical
4
unclonable function
4
function generation
4
generation in-memory
4
in-memory encryption
4

Similar Publications

The relentless emergence of antibiotic-resistant pathogens, particularly Gram-negative bacteria, highlights the urgent need for novel therapeutic interventions. Drug-resistant infections account for approximately 5 million deaths annually, yet the antibiotic development pipeline has largely stagnated. Venoms, representing a remarkably diverse reservoir of bioactive molecules, remain an underexploited source of potential antimicrobials.

View Article and Find Full Text PDF

A Faster Privacy-Preserving Medical Image Diagnosis Scheme with Machine Learning.

J Imaging Inform Med

January 2025

College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.

Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).

View Article and Find Full Text PDF

Vehicle-to-everything (V2X) communication has many benefits. It improves fuel efficiency, road safety, and traffic management. But it raises privacy and security concerns.

View Article and Find Full Text PDF

With the development of smart buildings, the risks of cyber-attacks against them have also increased. One of the popular and evolving protocols used for communication between devices in smart buildings, especially HVAC systems, is the BACnet protocol. Machine learning algorithms and neural networks require datasets of normal traffic and real attacks to develop intrusion detection (IDS) and prevention (IPS) systems that can detect anomalies and prevent attacks.

View Article and Find Full Text PDF

The growing significance of information technology requires advanced information storage and security solutions. While extending traditional 2D codes with additional parameters has led to promising 3D codes, increasing information capacity and security remains challenging. Herein, a 3D quick response (QR) cube platform that utilizes near-infrared (NIR)-to-NIR upconversion nanoparticles as light-emitting probes, benefiting from their photostability and low scattering properties.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!