activity
20172022
most citedSynthesizing Number Generators for Stochastic Computing using Mixed Integer Programming

2 citations · 4 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CR20222 cited

Verifiable Access Control for Augmented Reality Localization and Mapping

Shaowei Zhu, Hyo Jin Kim, Maurizio Monge +4

Localization and mapping is a key technology for bridging the virtual and physical worlds in augmented reality (AR). Localization and mapping works by creating and querying maps ma…

cs.CR2022

Homomorphically Encrypted Computation using Stochastic Encodings

Hsuan Hsiao, Vincent Lee, Brandon Reagen +1

Homomorphic encryption (HE) is a privacy-preserving technique that enables computation directly over ciphertext. Unfortunately, a key challenge for HE is that implementations can b…

cs.CV2021

Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors

Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim +9

As autonomous driving and augmented reality evolve, a practical concern is data privacy. In particular, these applications rely on localization based on user images. The widely ado…

cs.CR2021

SoK: Opportunities for Software-Hardware-Security Codesign for Next Generation Secure Computing

Deeksha Dangwal, Meghan Cowan, Armin Alaghi +3

Users are demanding increased data security. As a result, security is rapidly becoming a first-order design constraint in next generation computing systems. Researchers and practit…

cs.CR2021

Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption

Meghan Cowan, Deeksha Dangwal, Armin Alaghi +3

Homomorphic encryption (HE) is a privacy-preserving technique that enables computation directly on encrypted data. Despite its promise, HE has seen limited use due to performance o…

cs.CR2020

Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference

Brandon Reagen, Wooseok Choi, Yeongil Ko +4

As the application of deep learning continues to grow, so does the amount of data used to make predictions. While traditionally, big-data deep learning was constrained by computing…