A Privacy-preserving Data Transmission Protocol with Constant Interactions in E-health

被引:2
|
作者
Yang, Huijie [1 ]
Shen, Jian [1 ]
Obaidat, Mahammad [2 ]
Vijayakumar, Pandi [3 ]
Hsiao, Kuei-Fang [2 ]
机构
[1] Nanjing Univ Infor Sci & Tech, Sch Comp Sci, Nanjing, Peoples R China
[2] Univ Texas Permian Basin, Dept Comp Sci, Odessa, TX USA
[3] Univ Coll Engn Tindivanam, Dept Comp Sci & Engn, Tindivanam, India
关键词
e-health; privacy-preserving; oblivious transfers; data transmission;
D O I
10.1109/GLOBECOM48099.2022.10000994
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In recent years, to improve the quality of medical services in e-health systems, various types of sensors supporting collection and online/offline consultation have appeared in life; effectively facilitating doctors' disease prediction and consultation. However, data in e-health systems come from a wide range of sources and are mostly related to patient privacy. Therefore, how to ensure patient privacy and data confidentiality in data transmission is considered serious issues. In addition, the storage volume of cloud servers continues to grow, and how to guarantee that servers can quickly respond to requests has become a pressing problem. To this end, a privacy-preserving data transmission protocol is proposed, which only needs constant times interactions to complete the batching requests. In particular, a lightweight OTkn protocol is designed, employing the idea of matrix transformation, which effectively reduces the number of interactions while protecting the privacy of both communicating parties. The security and performance analysis indicate that the proposed protocol can be instantiated in e-health with high security and efficiency.
引用
收藏
页码:2248 / 2253
页数:6
相关论文
共 50 条
  • [1] Privacy-preserving aware data transmission for IoT-based e-health
    Boussada, Rihab
    Hamdane, Balkis
    Elhdhili, Mohamed Elhoucine
    Saidane, Leila Azouz
    [J]. COMPUTER NETWORKS, 2019, 162
  • [2] An Authentic and Privacy-Preserving Scheme Towards E-Health Data Transmission Service
    Fan, Qing
    Xie, Yumeng
    Zhang, Chuan
    Liu, Ximeng
    Zhu, Liehuang
    [J]. IEEE Transactions on Services Computing, 2024, 17 (05): : 1969 - 1982
  • [3] Privacy-Preserving Data Aggregation Scheme for E-Health
    Watkins, Matthew
    Dorsey, Colby
    Rennier, Daniel
    Polley, Timothy
    Sherif, Ahmed
    Elsersy, Mohamed
    [J]. PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND INTELLIGENT SYSTEMS, ICETIS 2022, VOL 2, 2023, 573 : 638 - 646
  • [4] Privacy-preserving fusion of IoT and big data for e-health
    Yang, Yang
    Zheng, Xianghan
    Guo, Wenzhong
    Liu, Ximeng
    Chang, Victor
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 86 : 1437 - 1455
  • [5] Privacy-preserving access to e-health systems
    Lax, Gianluca
    [J]. 11TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS (PETRA 2018), 2018, : 120 - 121
  • [6] Preserving data privacy in e-health
    Conti, Riccardo
    Lunardelli, Alessio
    Matteucci, Ilaria
    Mori, Paolo
    Petrocchi, Marinella
    [J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8431 : 366 - 392
  • [7] Preserving data privacy in e-Health
    [J]. 1600, Springer Verlag (8431):
  • [8] Collection of an e-Health Dataset and Anonymization with Privacy-Preserving Data Publishing Algorithms
    Kara, Burak Cem
    Eyupoglu, Can
    Uysal, Serkan
    Bayrakli, Selim
    [J]. ELECTRICA, 2023, 23 (03): : 658 - 665
  • [9] Towards a decentralized OSN for a privacy-preserving e-health system
    Benkaouz, Yahya
    Erradi, Mohammed
    [J]. 6TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN 2015)/THE 5TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2015), 2015, 63 : 284 - 291
  • [10] Privacy-Preserving Machine Learning for E-Health Applications: A Survey
    Romeo, Jared
    Abbass, Mahmoud
    Sherif, Ahmed
    Mamun, Mohammad M. R. Khan
    Elsersy, Mohamed
    Khalil, Kasem
    [J]. 2024 IEEE 3RD INTERNATIONAL CONFERENCE ON COMPUTING AND MACHINE INTELLIGENCE, ICMI 2024, 2024,