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Fixed-complexity Sphere Decoder (FSD) is a recently proposed technique for Multiple-Input Multiple-Output (MIMO) detection. It has several outstanding features such as constant throughput and large potential parallelism, which makes it…
With wide spread acceptance of virtualization, virtual machines (VMs) find their presence in various applications such as Network Address Translation (NAT) servers, firewall servers and MapReduce applications. Typically, in these…
The requirement for privacy-aware machine learning increases as we continue to use PII (Personally Identifiable Information) within machine training. To overcome these privacy issues, we can apply Fully Homomorphic Encryption (FHE) to…
A protocol for two-party secure function evaluation (2P-SFE) aims to allow the parties to learn the output of function $f$ of their private inputs, while leaking nothing more. In a sense, such a protocol realizes a trusted oracle that…
Polymorphism and inheritance make C++ suitable for writing complex software, but significantly increase the attack surface because the implementation relies on virtual function tables (vtables). These vtables contain function pointers that…
In recent years, there has been a growing concern with software integrity, that is, the assurance that software has not been tampered with on the path between developers and users. This path is represented by a software development pipeline…
Vertical Federated Learning (VFL) has emerged as one of the most predominant approaches for secure collaborative machine learning where the training data is partitioned by features among multiple parties. Most VFL algorithms primarily rely…
Previous schemes for designing secure branch prediction unit (SBPU) based on physical isolation can only offer limited security and significantly affect BPU's prediction capability, leading to prominent performance degradation. Moreover,…
Computing on encrypted data is a promising approach to reduce data security and privacy risks, with homomorphic encryption serving as a facilitator in achieving this goal. In this work, we accelerate homomorphic operations using the…
Fully-homomorphic encryption (FHE) enables computation on encrypted data while maintaining secrecy. Recent research has shown that such schemes exist even for quantum computation. Given the numerous applications of classical FHE…
GPUSPH was the first implementation of the weakly-compressible Smoothed Particle Hydrodynamics method to run entirely on GPU using CUDA. Version 5, released in June 2018, features a radical restructuring of the code, offering a more…
Hardware supply-chain attacks are raising significant security threats to the boot process of multiprocessor systems. This paper identifies a new, prevalent hardware supply-chain attack surface that can bypass multiprocessor secure boot due…
Address translation and protection play important roles in today's processors, supporting multiprocessing and enforcing security. Historically, the design of the address translation mechanisms has been closely tied to the instruction set.…
Today's microprocessors have grown significantly in complexity and functionality. Most of today's processors provide at least three levels of memory hierarchy, are heavily pipelined, and support some sort of cache coherency protocol. These…
Fully homomorphic encryption (FHE) is an encryption scheme which enables computation on encrypted data without revealing the underlying data. While there have been many advances in the field of FHE, developing programs using FHE still…
Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However,…
The widespread deployment of products powered by machine learning models is raising concerns around data privacy and information security worldwide. To address this issue, Federated Learning was first proposed as a privacy-preserving…
As modern analogue/mixed-signal design increasingly relies on optimization-in-the-loop flows, such as AI and LLM-based sizing agents that repeatedly invoke SPICE-efficient, accurate high-performance simulators have become an indispensable…
A time-efficient and comprehensive verification is a fundamental part of the design process for modern computing platforms, and it becomes ever more important and critical to optimize as the latter get ever more complex. SupeRFIVe is a…
Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated computing, FHE presents a promising opportunity for…