Compressive Privacy: From Information/Estimation Theory to Machine Learning

被引：34
|
作者：
Kung, S. Y. [1 ]
机构：
[1] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
来源：
IEEE SIGNAL PROCESSING MAGAZINE | 2017年 / 34卷 / 01期
关键词：
D O I：
10.1109/MSP.2016.2616720
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Most of our daily activities are now moving online in the big data era, with more than 25 billion devices already connected to the Internet, to possibly over a trillion in a decade. However, big data also bears a connotation of “big brother” when personal information (such as sales transactions) is being ubiquitously collected, stored, and circulated around the Internet, often without the data owner's knowledge. Consequently, a new paradigm known as online privacy or Internet privacy is becoming a major concern regarding the privacy of personal and sensitive data. © 1991-2012 IEEE.
引用
收藏
页码：94 / +
页数：11
相关论文
共 50 条
  • [1] Machine learning approaches for estimation of compressive strength of concrete
    Hadzima-Nyarko, Marijana
    Nyarko, Emmanuel Karlo
    Lu, Hongfang
    Zhu, Senlin
    [J]. EUROPEAN PHYSICAL JOURNAL PLUS, 2020, 135 (08):
  • [2] Machine learning approaches for estimation of compressive strength of concrete
    Marijana Hadzima-Nyarko
    Emmanuel Karlo Nyarko
    Hongfang Lu
    Senlin Zhu
    [J]. The European Physical Journal Plus, 135
  • [3] Information geometry and information theory in machine learning
    Ikeda, Kazushi
    Iwata, Kazunori
    [J]. NEURAL INFORMATION PROCESSING, PART II, 2008, 4985 : 295 - +
  • [4] Privacy Preserving Machine Learning with Limited Information Leakage
    Tang, Wenyi
    Qin, Bo
    Zhao, Suyun
    Zhao, Boning
    Xue, Yunzhi
    Chen, Hong
    [J]. NETWORK AND SYSTEM SECURITY, NSS 2019, 2019, 11928 : 352 - 370
  • [5] Compressive learning with privacy guarantees
    Chatalic, A.
    Schellekens, V
    Houssiau, F.
    de Montjoye, Y. A.
    Jacques, L.
    Gribonval, R.
    [J]. INFORMATION AND INFERENCE-A JOURNAL OF THE IMA, 2022, 11 (01) : 251 - 305
  • [6] A Compressive Multi-Kernel Method for Privacy-Preserving Machine Learning
    Chanyaswad, Thee
    Chang, J. Morris
    Kung, S. Y.
    [J]. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 4079 - 4086
  • [7] Estimation of Compressive Resistance of Briquettes Obtained from Groundnut Shells with Different Machine Learning Algorithms
    Kocer, Abdulkadir
    Kabas, Onder
    Zabava, Bianca Stefania
    [J]. APPLIED SCIENCES-BASEL, 2023, 13 (17):
  • [8] Information Theory and Its Relation to Machine Learning
    Hu, Bao-Gang
    [J]. PROCEEDINGS OF THE 2015 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT INFORMATION PROCESSING, 2015, 336 : 1 - 11
  • [9] Requirements for an Information Privacy Pedagogy based on the Constructivism Learning Theory
    Papaioannou, Thanos
    Tsohou, Aggeliki
    Karyda, Maria
    Karagiannis, Stylianos
    [J]. PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY, ARES 2022, 2022,
  • [10] Application of Personal Information Privacy Protection Based on Machine Learning Algorithm
    Lang, Fang
    Zhong, Yunfei
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
12345