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09/12/2022
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
Federated learning (FL) aims to perform privacy-preserving machine learn...
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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...
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11/25/2021
Gradient Inversion Attack: Leaking Private Labels in Two-Party Split Learning
Split learning is a popular technique used to perform vertical federated...
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04/06/2021
Enabling Inference Privacy with Adaptive Noise Injection
User-facing software services are becoming increasingly reliant on remot...
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11/16/2019
Defending Against Model Stealing Attacks with Adaptive Misinformation
Deep Neural Networks (DNNs) are susceptible to model stealing attacks, w...
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07/04/2019
TicToc: Enabling Bandwidth-Efficient DRAM Caching for both Hits and Misses in Hybrid Memory Systems
This paper investigates bandwidth-efficient DRAM caching for hybrid DRAM...
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07/04/2019
To Update or Not To Update?: Bandwidth-Efficient Intelligent Replacement Policies for DRAM Caches
This paper investigates intelligent replacement policies for improving t...
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06/06/2019
Lookout for Zombies: Mitigating Flush+Reload Attack on Shared Caches by Monitoring Invalidated Lines
OS-based page sharing is a commonly used optimization in modern systems ...
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02/12/2019
A Case for Superconducting Accelerators
As the scaling of conventional CMOS-based technologies slows down, there...
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01/28/2019
Improving Adversarial Robustness of Ensembles with Diversity Training
Deep Neural Networks are vulnerable to adversarial attacks even in setti...
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07/20/2018
CRAM: Efficient Hardware-Based Memory Compression for Bandwidth Enhancement
This paper investigates hardware-based memory compression designs to inc...
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05/08/2018