A framework for differentially-private knowledge graph embeddings

被引：0
|
作者：
Han, Xiaolin [1 ]
Dell'Aglio, Daniele [2 ,3 ]
Grubenmann, Tobias [4 ]
Cheng, Reynold [1 ]
Bernstein, Abraham [3 ]
机构：
[1] Department of Computer Science, The University of Hong Kong, Hong Kong
[2] Department of Computer Science, Aalborg University, Aalborg, Denmark
[3] Department of Informatics, University of Zurich, Zurich, Switzerland
[4] Department of Computer Science, University of Bonn, Bonn, Germany
来源：
Journal of Web Semantics | 2022年 / 72卷
基金：
瑞士国家科学基金会;
关键词：
AS-links - Data mining tasks - Differential privacies - Embedding method - Embeddings - Graph embeddings - Graph-based data minings - Knowledge graph embedding - Knowledge graphs - Link prediction;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [31] Differentially-Private Deep Learning from an Optimization Perspective
    Xiang, Liyao
    Yang, Jingbo
    Li, Baochun
    IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2019), 2019, : 559 - 567
  • [32] The Cost of Privacy in Asynchronous Differentially-Private Machine Learning
    Farokhi, Farhad
    Wu, Nan
    Smith, David
    Kaafar, Mohamed Ali
    IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2021, 16 (16) : 2118 - 2129
  • [33] GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces
    Lamp, Josephine
    Derdzinski, Mark
    Hannemann, Christopher
    van der Linden, Joost
    Feng, Lu
    Wang, Tianhao
    Evans, David
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [34] Separate Spaces Framework for Entities and Relations Embeddings in Knowledge Graph
    Huang, Qinhua
    Ouyang, Weimin
    INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT I, 2018, 10954 : 378 - 388
  • [35] A Knowledge Transfer Framework for Differentially Private Sparse Learning
    Wang, Lingxiao
    Gu, Quanquan
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 6235 - 6242
  • [36] cpSGD: Communication-efficient and differentially-private distributed SGD
    Agarwal, Naman
    Suresh, Ananda Theertha
    Yu, Felix
    Kumar, Sanjiv
    McMahan, H. Brendan
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [37] SafePath: Differentially-private publishing of passenger trajectories in transportation systems
    Al-Hussaeni, Khalil
    Fung, Benjamin C. M.
    Iqbal, Farkhund
    Dagher, Gaby G.
    Park, Eun G.
    COMPUTER NETWORKS, 2018, 143 : 126 - 139
  • [38] Differentially-Private Software Analytics for Mobile Apps: Opportunities and Challenges
    Zhang, Hailong
    Latif, Sufian
    Bassily, Raef
    Rountev, Atanas
    PROCEEDINGS OF THE 4TH ACM SIGSOFT INTERNATIONAL WORKSHOP ON SOFTWARE ANALYTICS (SWAN'18), 2018, : 26 - 29
  • [39] Differentially-Private Release of Check-in Data for Venue Recommendation
    Riboni, Daniele
    Bettini, Claudio
    2014 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS (PERCOM), 2014, : 190 - 198
  • [40] Differentially-Private Software Frequency Profiling under Linear Constraints
    Zhang, Hailong
    Hao, Yu
    Latif, Sufian
    Bassily, Raef
    Rountev, Atanas
    PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL, 2020, 4 (OOPSLA):
12345