Evaluation of Synthetic Data for Privacy-Preserving Machine Learning

被引：0
|
作者：
Hittmeir, Markus [1 ]
Ekelhart, Andreas [1 ]
Mayer, Rudolf [1 ]
机构：
[1] SBA Res, Vienna, Austria
来源：
ERCIM NEWS | 2020年 / 123期
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The generation of synthetic data is widely considered to be an effective way of ensuring privacy and reducing the risk of disclosing sensitive information in micro-data. We analysed these risks and the utility of synthetic data for machine learning tasks. Our results demonstrate the suitability of this approach for privacy-preserving data publishing.
引用
收藏
页码：30 / 31
页数：2
相关论文
共 50 条
  • [41] GENoPPML - a framework for genomic privacy-preserving machine learning
    Carpov, Sergiu
    Gama, Nicolas
    Georgieva, Mariya
    Jetchev, Dimitar
    2022 IEEE 15TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (IEEE CLOUD 2022), 2022, : 532 - 542
  • [42] SecureML: A System for Scalable Privacy-Preserving Machine Learning
    Mohassel, Payman
    Zhang, Yupeng
    2017 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP), 2017, : 19 - 38
  • [43] Privacy-Preserving Distributed Machine Learning Made Faster
    Jiang, Zoe L.
    Gu, Jiajing
    Wang, Hongxiao
    Wu, Yulin
    Fang, Junbin
    Yiu, Siu-Ming
    Luo, Wenjian
    Wang, Xuan
    PROCEEDINGS OF THE INAUGURAL ASIACCS 2023 WORKSHOP ON SECURE AND TRUSTWORTHY DEEP LEARNING SYSTEMS, SECTL, 2022,
  • [44] Privacy-Preserving Machine Learning Using EtC Images
    Kawamura, Ayana
    Kinoshita, Yuma
    Kiya, Hitoshi
    INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY (IWAIT) 2020, 2020, 11515
  • [45] BLAZE: Blazing Fast Privacy-Preserving Machine Learning
    Patra, Arpita
    Suresh, Ajith
    27TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2020), 2020,
  • [46] Practical Secure Aggregation for Privacy-Preserving Machine Learning
    Bonawitz, Keith
    Ivanov, Vladimir
    Kreuter, Ben
    Marcedone, Antonio
    McMahan, H. Brendan
    Patel, Sarvar
    Ramage, Daniel
    Segal, Aaron
    Seth, Karn
    CCS'17: PROCEEDINGS OF THE 2017 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, 2017, : 1175 - 1191
  • [47] Privacy-Preserving Machine Learning as a Service: Challenges and Opportunities
    Zhang, Qiao
    Xiang, Tao
    Cai, Yifei
    Zhao, Zhichao
    Wang, Ning
    Wu, Hongyi
    IEEE NETWORK, 2023, 37 (06): : 214 - 223
  • [48] Client-Aided Privacy-Preserving Machine Learning
    Miao, Peihan
    Shi, Xinyi
    Wu, Chao
    Xu, Ruofan
    SECURITY AND CRYPTOGRAPHY FOR NETWORKS, PT I, SCN 2024, 2024, 14973 : 207 - 229
  • [49] GFL: Federated Learning on Non-IID data via Privacy-preserving Synthetic data
    Cheng, Yihang
    Zhang, Lan
    Li, Anran
    2023 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS, PERCOM, 2023, : 61 - 70
  • [50] Try before You Buy: Privacy-preserving Data Evaluation on Cloud-based Machine Learning Data Marketplace
    Song, Qiyang
    Cao, Jiahao
    Sun, Kun
    Li, Qi
    Xu, Ke
    37TH ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE, ACSAC 2021, 2021, : 260 - 272
12345