联邦可视化：一种隐私保护的可视化新模型

魏雅婷; 王智勇; 周舒悦; 陈为

doi:10.11959/j.issn.2096-6652.201946

您当前的位置：

首页 >

文章列表页 >

联邦可视化：一种隐私保护的可视化新模型

学术论文 | 更新时间：2024-06-05

- 联邦可视化：一种隐私保护的可视化新模型
- Federated visualization:a new model for privacy-preserving visualization
- 智能科学与技术学报 2019年1卷第4期页码：415-420
- 作者机构：
- 作者简介：
  
  [ "魏雅婷（1994- ），女，浙江大学计算机辅助设计与图形学国家重点实验室博士生，主要研究方向为可视分析、可视设计、感知、认知" ]
  [ "王智勇（1996- ），男，浙江大学计算机辅助设计与图形学国家重点实验室硕士生，主要研究方向为可视分析、机器学习" ]
  [ "周舒悦（1996- ），女，浙江大学计算机辅助设计与图形学国家重点实验室硕士生，主要研究方向为可视化、可视分析、感知认知" ]
  [ "陈为（1976- ），男，博士，浙江大学计算机辅助设计与图形学国家重点实验室教授，博士生导师，CCF 高级会员，主要研究方向为可视化、可视分析、大数据分析、人机混合智能" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61772456)
- DOI：10.11959/j.issn.2096-6652.201946
  中图分类号： TP183
- 网络出版日期：2019-12，
  
  纸质出版日期：2019-12-20
- 稿件说明：
移动端阅览
魏雅婷, 王智勇, 周舒悦, 等. 联邦可视化：一种隐私保护的可视化新模型[J]. 智能科学与技术学报, 2019,1(4):415-420.

Yating WEI, Zhiyong WANG, Shuyue ZHOU, et al. Federated visualization:a new model for privacy-preserving visualization[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 415-420.
魏雅婷, 王智勇, 周舒悦, 等. 联邦可视化：一种隐私保护的可视化新模型[J]. 智能科学与技术学报, 2019,1(4):415-420. DOI： 10.11959/j.issn.2096-6652.201946.

Yating WEI, Zhiyong WANG, Shuyue ZHOU, et al. Federated visualization:a new model for privacy-preserving visualization[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 415-420. DOI： 10.11959/j.issn.2096-6652.201946.

摘要

概述了联邦可视化的概念、框架、方法与应用。联邦可视化框架能够在不进行数据整合的情况下，针对具体任务和特定场景进行加密训练，得出反映全体数据特征的可视化模型。联邦可视化是联邦学习框架在可视化领域的拓展应用，主要强调在保障数据隐私的前提下，互利共赢的联邦协作方式在对多数据源数据进行可视分析方面的应用，以打破各领域、各行业的数据壁垒，实现数据与知识的共享。

Abstract

The concept

architecture

methods and applications of federated visualization were introduced.The federated visualization framework is capable of encrypting and training a visual model that reflect the characteristics of the entire data for specific tasks and scenarios.The federated visualization framework is an extension and application of federated learning

which emphasized using mutual benefit and win-win federal cooperation to visually analyze multi-source data under the premise of ensuring data privacy

towards breaking down data barriers in various fields and industries and realizing the sharing of data and knowledge.

关键词

Keywords

references

SWEENEY L . K-anonymity:a model for protecting privacy [J ] . International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems , 2002 , 10 ( 5 ): 557 - 570 .

MACHANAVAJJHALA A , GEHRHE J , KIFER D , et al . L-diversity:privacy beyond k-anonymity [J ] . ACM Transactions on Knowledge Discovery from Data , 2006 , 1 ( 1 ):3.

LI N H , LI T C , VENKATASUBRAMANIAN S . T-closeness:privacy beyond k-anonymity and l-diversity [C ] // 2007 IEEE 23rd International Conference on Data Engineering,April 15-20,2007,Istanbul,Turkey . Piscataway:IEEE Press , 2007 : 106 - 115 .

COMAS J S , FERRERT J D . Differential privacy via t-closeness in data publishing [C ] // 2013 Eleventh Annual Conference on Privacy,Security and Trust,July 10-12,2013,Tarragona,Spain . Piscataway:IEEE Press , 2013 : 27 - 35 .

DASGUPTA A , KOSARA R . Adaptive privacy-preserving visualization using parallel coordinates [J ] . IEEE Transactions on Visualization and Computer Graphics , 2011 , 17 ( 12 ): 2241 - 2248 .

CHOU J K , WANG Y , MA K L . Privacy preserving visualization:a study on event sequence data [J ] .,2018. Computer Graphics Forum , 2018 .

WANG X M , CHEN W , CHOU J K , et al . GraphProtector:a visual interface for employing and assessing multiple privacy preserving graph algorithms [J ] . IEEE Transactions on Visualization and Computer Graphics , 2018 , 25 ( 1 ): 193 - 203 .

WANG X M , CHOU J K , CHEN W , et al . A utility-aware visual approach for anonymizing multi-attribute tabular data [J ] . IEEE Transactions on Visualization and Computer Graphics , 2017 , 24 ( 1 ): 351 - 360 .

DASGUPTA A , KOSARA R , CHEN M . Guess me if you can:a visual uncertainty model for transparent evaluation of disclosure risks in privacy-preserving data visualization [C ] // The 16th IEEE Symposium on Visualization for Cyber Security,October 23,2019,Vancouver,Canada . Piscataway:IEEE Press , 2019 .

OKSANEN J , BERGMAN C , SAINIO J , et al . Methods for deriving and calibrating privacy-preserving heat maps from mobile sports tracking application data [J ] . Journal of Transport Geography , 2015 , 48 : 135 - 144 .

DASGUPTA A , KOSARA R . Adaptive privacy-preserving visualization using parallel coordinates [J ] . IEEE Transactions on Visualization and Computer Graphics , 2011 , 17 ( 12 ): 2241 - 2248 .

AMERSHI S , CAKMAK M , KNOX W B , et al . Power to the people:the role of humans in interactive machine learning [J ] . AI Magazine , 2014 , 35 ( 4 ): 105 - 120 .

TZENG F Y , MA K L . Opening the black box-data driven visualization of neural networks [C ] // IEEE Visualization,October 23-28,2005,Minneapolis,USA . Piscataway:IEEE Press , 2005 : 383 - 390 .

JAEGUL C , LIU S X . Visual analytics for explainable deep learning [J ] . IEEE Computer Graphics and Applications , 2018 , 38 ( 4 ): 84 - 92 .

HOHMAN F M , KAHNG M , PIENTA R , et al . Visual analytics in deep learning:an interrogative survey for the next frontiers [J ] .,2018. IEEE Transactions on Visualization and Computer Graphics , 2018 .

LIU S X , XIAO J N , LIU J L , et al . Visual diagnosis of tree boosting methods [J ] . IEEE Transactions on Visualization and Computer Graphics , 2018 , 24 ( 1 ): 163 - 173 .

ALSALLAKH B , LOURABLOO A , YE M , et al . Do convolutional neural networks learn class hierarchy [J ] . IEEE Transactions on Visualization and Computer Graphics , 2018 , 24 ( 1 ): 152 - 162 .

LIU M C , SHI J X , LI Z , et al . Towards better analysis of deep convolutional neural networks [J ] . IEEE Transactions on Visualization and Computer Graphics , 2016 , 23 ( 1 ): 91 - 100 .

MING Y , CAO S Z , ZHANG R X , et al . Understanding hidden memories of recurrent neural networks [C ] // 2017 IEEE Conference on Visual Analytics Science and Technology,October 1,Phoenix,USA . Piscataway:IEEE Press , 2017 : 13 - 24 .

KAHNG M , THORAT N , CHAU DHP , et al . Gan lab:understanding complex deep generative models using interactive visual experimentation [J ] . IEEE Transactions on Visualization and Computer Graphics , 2018 , 25 ( 1 ): 1 - 11 .

WANG J , GOU L , SHEN H W , et al . Dqnviz:A visual analytics approach to understand deep q-networks [J ] . IEEE Transactions on Visualization and Computer Graphics , 2018 , 25 ( 1 ): 288 - 298 .

LIU M C , SHI J X , CAO K , et al . Analyzing the training processes of deep generative models [J ] . IEEE Transactions on Visualization and Computer Graphics , 2018 , 24 ( 1 ): 77 - 87 .

HUANG Z S , ZHAO Y , CHEN W , et al . A natural-language-based visual query approach of uncertain human trajectories [J ] . IEEE Transactions on Visualization and Computer Graphics , 2020 , 26 ( 1 ): 1256 - 1266 .

YU B W , SILVA C T . FlowSense:a natural language interface for visual data exploration within a dataflow system [J ] .,2019. IEEE Transactions on Visualization and Computer Graphics , 2019 .

ZHENG J X S , PAWAR S , MATTHEW G D F . Graph drawing by stochastic gradient descent [J ] . IEEE Transactions on Visualization and Computer Graphics , 2018 : 1 - 1 .

WANG Y , JIN Z H , WANG Q W , et al . DeepDrawing:a deep learning approach to graph drawing [J ] . IEEE Transactions on Visualization and Computer Graphics , 2019 , 26 ( 1 ): 676 - 686 .

KWON O H , MA K L . A deep generative model for graph layout [J ] . arXiv preprint , 2019 ,arXiv:1904.12225.

CHEN Z T , WANG Y , WANG Q , et al . Towards automated infographic design:deep learning-based auto-extraction of extensible timeline [J ] . IEEE Transactions on Visualization and Computer Graphics , 2019 , 26 ( 1 ): 917 - 926 .

CUI W , ZHANG X , WANG Y , et al . Text-to-viz:automatic generation of infographics from proportion-related natural language statements [J ] . IEEE Transactions on Visualization and Computer Graphics , 2019 , 26 ( 1 ): 906 - 916 .

WANG Y , SUN Z , ZHANG H , et al . DataShot:automatic generation of fact sheets from tabular data [J ] . IEEE Transactions on Visualization and Computer Graphics , 2019 , 26 ( 1 ): 895 - 905 .

MCMAHAN H B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [J ] . International Conference on Artificial Intelligence , 2017 , 20 ( 22 ): 1273 - 1282 .

KONECHY J , MCMAHAN H B , RAMAGE D , et al . Federated optimization:distributed machine learning for on-device intelligence [J ] . arXiv preprint , 2016 ,arXiv:1610.02527.

MCMAHAN H B , RAMAGE D , TALWAR K , et al . Learning differentially private recurrent language models [J ] . arXiv preprint , 2017 ,arXiv:1710.06963.

GEYER R C , KLEIN T , NABI M . Differentially private federated learning:a client level perspective [J ] . arXiv preprint , 2017 arXiv:1712.07557.

HUANG L , LIU D B . Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records [J ] . Journal of Biomedical Informatics , 2019 , 99 :103291.

HUANG L , YIN Y F , FU Z , et al . LoAdaBoost:loss-based AdaBoost federated machine learning on medical data [J ] . arXiv preprint , 2018 arXiv:1811.12629.

杨强 . GDPR对AI的挑战和基于联邦迁移学习的对策 [J ] . 中国人工智能学会通讯 , 2018 , 8 : 1 - 8 .

YANG Q . The challenge of GDPR to AI and the countermeasures based on federal immigration learning [J ] . China Artificial Intelligence Society Newsletter , 2018 , 8 : 1 - 8 .

浏览量

270

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于多模态大语言模型的数字厨师与智能烹饪系统

新AI与智能科技前沿：从PAPAI++到新产业生态

智能科技与K21教育：未来社会的未来学校与未来师生

数字教师与平行教育：关于ChatGPT之后教学变革的探讨

融合音画同步的唇形合成研究