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08/04/2023
Privacy Preserving In-memory Computing Engine
Privacy has rapidly become a major concern/design consideration. Homomor...
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07/09/2023
Towards Fast and Scalable Private Inference
Privacy and security have rapidly emerged as first order design constrai...
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04/20/2023
DeepReShape: Redesigning Neural Networks for Efficient Private Inference
The increasing demand for privacy and security has driven the advancemen...
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04/11/2023
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
Secure computation is of critical importance to not only the DoD, but ac...
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03/30/2023
RPU: The Ring Processing Unit
Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many i...
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11/23/2022
A Garbled Circuit Accelerator for Arbitrary, Fast Privacy-Preserving Computation
Privacy and security have rapidly emerged as priorities in system design...
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07/14/2022
Characterizing and Optimizing End-to-End Systems for Private Inference
Increasing privacy concerns have given rise to Private Inference (PI). I...
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05/13/2022
Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference
This paper proposes Impala, a new cryptographic protocol for private inf...
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03/24/2022
Verifiable Access Control for Augmented Reality Localization and Mapping
Localization and mapping is a key technology for bridging the virtual an...
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03/04/2022
Homomorphically Encrypted Computation using Stochastic Encodings
Homomorphic encryption (HE) is a privacy-preserving technique that enabl...
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02/04/2022
Selective Network Linearization for Efficient Private Inference
Private inference (PI) enables inference directly on cryptographically s...
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11/04/2021
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale
The privacy concerns of providing deep learning inference as a service h...
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07/26/2021
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Privacy concerns in client-server machine learning have given rise to pr...
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06/17/2021
Sphynx: ReLU-Efficient Network Design for Private Inference
The emergence of deep learning has been accompanied by privacy concerns ...
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06/15/2021
Circa: Stochastic ReLUs for Private Deep Learning
The simultaneous rise of machine learning as a service and concerns over...
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05/09/2021
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors
As autonomous driving and augmented reality evolve, a practical concern ...
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05/02/2021
SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing
Users are demanding increased data security. As a result, security is ra...
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03/02/2021
DeepReDuce: ReLU Reduction for Fast Private Inference
The recent rise of privacy concerns has led researchers to devise method...
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01/19/2021
Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption
Homomorphic encryption (HE) is a privacy-preserving technique that enabl...
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06/15/2020
CryptoNAS: Private Inference on a ReLU Budget
Machine learning as a service has given raise to privacy concerns surrou...
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05/31/2020
Cheetah: Optimizations and Methods for PrivacyPreserving Inference via Homomorphic Encryption
As the application of deep learning continues to grow, so does the amoun...
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01/08/2020
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Neural personalized recommendation is the corner-stone of a wide collect...
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12/30/2019
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Personalized recommendation systems leverage deep learning models and ac...
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06/06/2019
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of...
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11/13/2017