.png){kind=icon}
返回
Deep Private-Feature Extraction
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2018.2878698.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a users device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy trade-off. We then implement and evaluate the performance of DPFE on smartphones to understand its complexity, resource demands, and efficiency trade-offs. Our results on benchmark image datasets demonstrate that under moderate resource utilization, DPFE can achieve high accuracy for primary tasks while preserving the privacy of sensitive information.
Keyword:
Data privacy
Feature extraction
Privacy
Data models
Task analysis
Training
Feature extraction
privacy
information theory
deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.7W
机构
引用论文
.png){kind=icon}
Modeling and Simulation of Main Control Valve Mechanism of LNG Ship Steam TurbineLNG船舶汽轮机主控阀机构建模与仿真
OAAI
OAAI分享收藏Toward quasi-DC conductivity of warm dense matter measured by single-shot terahertz spectroscopyOAAI
OAAI分享收藏Switching Currents Limited by Single Phase Slips in One-Dimensional Superconducting Al NanowiresOAAI
OAAI分享收藏
查看更多内容