Zobrazeno 1 - 10
of 306
pro vyhledávání: '"Wang, Chenghong"'
Autor:
Duan, Shijin, Wang, Chenghong, Peng, Hongwu, Luo, Yukui, Wen, Wujie, Ding, Caiwen, Xu, Xiaolin
As privacy-preserving becomes a pivotal aspect of deep learning (DL) development, multi-party computation (MPC) has gained prominence for its efficiency and strong security. However, the practice of current MPC frameworks is limited, especially when
Externí odkaz:
http://arxiv.org/abs/2406.02629
Secure collaborative analytics (SCA) enable the processing of analytical SQL queries across multiple owners' data, even when direct data sharing is not feasible. Although essential for strong privacy, the large overhead from data-oblivious primitives
Externí odkaz:
http://arxiv.org/abs/2404.18388
Autor:
Peng, Hongwu, Ran, Ran, Luo, Yukui, Zhao, Jiahui, Huang, Shaoyi, Thorat, Kiran, Geng, Tong, Wang, Chenghong, Xu, Xiaolin, Wen, Wujie, Ding, Caiwen
The growth of Graph Convolution Network (GCN) model sizes has revolutionized numerous applications, surpassing human performance in areas such as personal healthcare and financial systems. The deployment of GCNs in the cloud raises privacy concerns d
Externí odkaz:
http://arxiv.org/abs/2309.14331
Autor:
Peng, Hongwu, Huang, Shaoyi, Zhou, Tong, Luo, Yukui, Wang, Chenghong, Wang, Zigeng, Zhao, Jiahui, Xie, Xi, Li, Ang, Geng, Tony, Mahmood, Kaleel, Wen, Wujie, Xu, Xiaolin, Ding, Caiwen
The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have high computation and communicat
Externí odkaz:
http://arxiv.org/abs/2308.10134
Autor:
Peng, Hongwu, Zhou, Shanglin, Luo, Yukui, Xu, Nuo, Duan, Shijin, Ran, Ran, Zhao, Jiahui, Wang, Chenghong, Geng, Tong, Wen, Wujie, Xu, Xiaolin, Ding, Caiwen
Publikováno v:
DAC 2023
Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison protocol overhead from the non-linear operators. This work presents PASNe
Externí odkaz:
http://arxiv.org/abs/2306.15513
Autor:
Peng, Hongwu, Zhou, Shanglin, Luo, Yukui, Xu, Nuo, Duan, Shijin, Ran, Ran, Zhao, Jiahui, Huang, Shaoyi, Xie, Xi, Wang, Chenghong, Geng, Tong, Wen, Wujie, Xu, Xiaolin, Ding, Caiwen
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns. To address these issues, secure Two-party computation (2PC) has been proposed as a means of enabling privacy-preserving DL computation. However, in pra
Externí odkaz:
http://arxiv.org/abs/2302.02292
Autor:
Peng, Hongwu, Zhou, Shanglin, Luo, Yukui, Duan, Shijin, Xu, Nuo, Ran, Ran, Huang, Shaoyi, Wang, Chenghong, Geng, Tong, Li, Ang, Wen, Wujie, Xu, Xiaolin, Ding, Caiwen
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving DL computation. In practic
Externí odkaz:
http://arxiv.org/abs/2209.09424
In this paper, we consider secure outsourced growing databases that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view, such that they can use only the materialized view to process qu
Externí odkaz:
http://arxiv.org/abs/2203.05084
Publikováno v:
In Surface & Coatings Technology 30 October 2024 494 Part 1
Publikováno v:
In Energy 30 September 2024 304