Related papers: SecDDR: Enabling Low-Cost Secure Memories by Prote…

Currently, an increasing number of users and enterprises are storing their data in the cloud but do not fully trust cloud providers with their data in plaintext form. To address this concern, they encrypt their data before uploading it to…

A popular run-time attack technique is to compromise the control-flow integrity of a program by modifying function return addresses on the stack. So far, shadow stacks have proven to be essential for comprehensively preventing return…

Many high end and next generation computing systems to incorporated alternative memory technologies to meet performance goals. Since these technologies present distinct advantages and tradeoffs compared to conventional DDR* SDRAM, such as…

Radio Access Network faces challenges from privacy and flexible wide area and local area network access. RAN is limited from providing local service directly due to centralized design of cellular network and concerns of user privacy and…

Memory-safety violations in C and C++ programs continue to enable sophisticated exploitation techniques such as control-flow hijacking and data-oriented attacks. Existing hardware defenses either rely on address space layout randomization…

Decentralized learning (DL) faces increased vulnerability to privacy breaches due to sophisticated attacks on machine learning (ML) models. Secure aggregation is a computationally efficient cryptographic technique that enables multiple…

CPU registers are small discrete storage units, used to hold temporary data and instructions within the CPU. Registers are not addressable in the same way memory is, which makes them immune from memory attacks and manipulation by other…

The remarkable proliferation of deep learning across various industries has underscored the importance of data privacy and security in AI pipelines. As the evolution of sophisticated Membership Inference Attacks (MIAs) threatens the secrecy…

Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs).Although the state-of-the-art method Neural Attention Distillation (NAD) can…

Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extract secret…

The challenge of fairness arises when Automatic Speech Recognition (ASR) systems do not perform equally well for all sub-groups of the population. In the past few years there have been many improvements in overall speech recognition…

After a plethora of high-profile RowHammer attacks, CPU and DRAM vendors scrambled to deliver what was meant to be the definitive hardware solution against the RowHammer problem: Target Row Refresh (TRR). A common belief among practitioners…

This paper address a new problem in RFID authentication research for the first time. That is, existing RFID authentication schemes generally assume that the backend server is absolutely secure, however, this assumption is rarely tenable in…

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval but faces challenges on edge devices due to high storage, energy, and latency demands. Computing-in-Memory (CIM) offers a…

With the rapid development of general cloud services, more and more individuals or collectives use cloud platforms to store data. Assured data deletion deserves investigation in cloud storage. In time-sensitive data storage scenarios, it is…

Federated learning enables collaborative machine learning while preserving data privacy. However, the rise of federated unlearning, designed to allow clients to erase their data from the global model, introduces new privacy concerns.…

We propose an integrated architecture for smart grids, that supports data aggregation and access control. Data can be aggregated by home area network, building area network and neighboring area network in such a way that the privacy of…

Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire rank from serving memory requests…

Dynamic Random Access Memory (DRAM) is pervasive in computer systems. Cell vulnerabilities caused by unintended phenomena (forced retention failure, latency alteration, rowhammer and rowpress) lead to unintended bit flips in memory. These…

The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing…