Zobrazeno 1 - 10
of 458
pro vyhledávání: '"Xiong, Wenjie"'
Autor:
Ji, Jun, Xi, Zichen, Srijanto, Bernadeta R., Kravchenko, Ivan I., Jin, Ming, Xiong, Wenjie, Shao, Linbo
Multiply-accumulation (MAC) is a crucial computing operation in signal processing, numerical simulations, and machine learning. This work presents a scalable, programmable, frequency-domain parallel computing leveraging gigahertz (GHz)-frequency acou
Externí odkaz:
http://arxiv.org/abs/2409.02689
Autor:
Erata, Ferhat, Chiu, TingHung, Etim, Anthony, Nampally, Srilalith, Raju, Tejas, Ramu, Rajashree, Piskac, Ruzica, Antonopoulos, Timos, Xiong, Wenjie, Szefer, Jakub
This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness an
Externí odkaz:
http://arxiv.org/abs/2405.05193
Autor:
Tiwari, Trishita, Gururangan, Suchin, Guo, Chuan, Hua, Weizhe, Kariyappa, Sanjay, Gupta, Udit, Xiong, Wenjie, Maeng, Kiwan, Lee, Hsien-Hsin S., Suh, G. Edward
In today's machine learning (ML) models, any part of the training data can affect the model output. This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access cont
Externí odkaz:
http://arxiv.org/abs/2306.03235
Autor:
Lam, Maximilian, Johnson, Jeff, Xiong, Wenjie, Maeng, Kiwan, Gupta, Udit, Li, Yang, Lai, Liangzhen, Leontiadis, Ilias, Rhu, Minsoo, Lee, Hsien-Hsin S., Reddi, Vijay Janapa, Wei, Gu-Yeon, Brooks, David, Suh, G. Edward
On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on e
Externí odkaz:
http://arxiv.org/abs/2301.10904
Autor:
Hashemi, Hanieh, Xiong, Wenjie, Ke, Liu, Maeng, Kiwan, Annavaram, Murali, Suh, G. Edward, Lee, Hsien-Hsin S.
Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest or news feed. State-of-the-art recommendation models rely on sparse and
Externí odkaz:
http://arxiv.org/abs/2212.06264
Autor:
Zeng, Wenxuan, Li, Meng, Xiong, Wenjie, Tong, Tong, Lu, Wen-jie, Tan, Jin, Wang, Runsheng, Huang, Ru
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference. However, existing neural network architectures, including Vision Transformers (ViTs), are not des
Externí odkaz:
http://arxiv.org/abs/2211.13955
Autor:
Kariyappa, Sanjay, Guo, Chuan, Maeng, Kiwan, Xiong, Wenjie, Suh, G. Edward, Qureshi, Moinuddin K, Lee, Hsien-Hsin S.
Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners. To this end, FL requires the data owners to perform training locally and share the gradient updates (instead of the private
Externí odkaz:
http://arxiv.org/abs/2209.05578
Autor:
Luo, Mulong, Xiong, Wenjie, Lee, Geunbae, Li, Yueying, Yang, Xiaomeng, Zhang, Amy, Tian, Yuandong, Lee, Hsien-Hsin S., Suh, G. Edward
The aggressive performance optimizations in modern microprocessors can result in security vulnerabilities. For example, timing-based attacks in processor caches can steal secret keys or break randomization. So far, finding cache-timing vulnerabilitie
Externí odkaz:
http://arxiv.org/abs/2208.08025
Autor:
Frank, Florian, Xiong, Wenjie, Anagnostopoulos, Nikolaos Athanasios, Schaller, André, Arul, Tolga, Koushanfar, Farinaz, Katzenbeisser, Stefan, Ruhrmair, Ulrich, Szefer, Jakub
The ubiquity and pervasiveness of modern Internet of Things (IoT) devices opens up vast possibilities for novel applications, but simultaneously also allows spying on, and collecting data from, unsuspecting users to a previously unseen extent. This p
Externí odkaz:
http://arxiv.org/abs/2208.02125
Publikováno v:
In Green Energy & Environment September 2024 9(9):1440-1448
