CoChat
Sign Up
Cited by 61Privacy-Preserving Technologies in DataAdversarial Robustness in Machine LearningIoT and Edge/Fog Computing

The DOI matches its Crossref record; its reference list is not available.

A Security- and Privacy-Preserving Approach Based on Data Disturbance for Collaborative Edge Computing in Social IoT Systems

Zhang, Peiying, Wang, Yaqi, Kumar, Neeraj, Jiang, Chunxiao, Shi, Guowei·2022·IEEE Transactions on Computational Social Systems
DOI 10.1109/tcss.2021.3092746
Open this paper in CoChat — free account

Abstract

The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people’s lives in all directions. The rapid development of the IoT technology accelerates the process of the era of “Internet of everything” but also changes the role of terminal equipment at the edge of the network. It has changed from a single data user to a dual role of both producing and using data. And collaborative edge computing (CEC) has been born in time. CEC itself can not only solve the problem of computing and storage but also combines with the deep learning (DL) model to make full use of edge computing ability. However, as the core of DL, the robustness of neural network is often not high. In addition, edge devices of CEC are facing a highly dynamic environment, which can easily cause the edge network to be attacked by malicious devices. Therefore, user privacy protection and security issues for CEC deserve more attention. To avoid privacy leakage and security crisis of CEC in social IoT systems, a data protection method based on data disturbance method and adversarial training viewpoint is introduced in this article. Besides, a new adversarial sample generation method based on the firefly algorithm (FA) is proposed. This method reduces the time complexity of traditional by an order for magnitude compared with traditional generative adversarial network (GAN) generation. Since sentences, information on CEC in the IoT syst…

Overview

Integrity signals

Registry

Crossref · 10.1109/tcss.2021.3092746

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Data disturbance techniques have the ability to interfere with specific data while ensuring overall data availability.

    “Data disturbance techniques have the ability to interfere with specific data while ensuring overall data availability [117].”

    From Machine Unlearning: A Survey (2024) · cites this paper as [117]

Citations

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 48 · Cited by 61

Research works printed but not linked: 49

  1. 10.1016/j.aei.2020.101043 · DOI
  2. 10.1109/JIOT.2021.3068158 · DOI
  3. 10.1109/TMC.2020.3012509 · DOI
  4. 10.1109/JIOT.2021.3064468 · DOI
  5. 10.1007/s10916-014-0116-z · DOI
  6. 10.1109/JIOT.2020.3017742 · DOI
  7. 10.1109/TKDE.2019.2923638 · DOI
  8. 10.1109/ACCESS.2020.3011904 · DOI
  9. 10.1109/ACCESS.2020.3041872 · DOI
  10. 10.1109/ACCESS.2020.3007002 · DOI
  11. 10.1007/s11276-017-1549-3 · DOI
  12. 10.1007/s10586-018-2181-4 · DOI
  13. 10.1049/iet-com.2019.0172 · DOI
  14. 10.1109/JIOT.2020.3021457 · DOI
  15. 10.1109/JIOT.2018.2847302 · DOI
  16. 10.1007/s00500-020-04955-z · DOI
  17. 10.1007/s11277-020-07705-4 · DOI
  18. 10.1109/MVT.2020.3019650 · DOI
  19. 10.1109/MWC.2017.1800094 · DOI
  20. 10.1109/TMC.2020.3025116 · DOI
  21. 10.1109/TVT.2020.3035341 · DOI · Paper page
  22. 10.1109/MNET.2019.1800239 · DOI
  23. 10.1016/j.comcom.2020.08.006 · DOI
  24. 10.1109/TII.2017.2771382 · DOI
  25. 10.1016/j.comcom.2020.09.012 · DOI
  26. 10.1007/s12083-014-0248-4 · DOI
  27. 10.1186/s13677-020-00225-3 · DOI
  28. 10.1016/j.comcom.2020.11.018 · DOI
  29. 10.1109/TIFS.2017.2758756 · DOI
  30. 10.1109/MCE.2018.2880824 · DOI
  31. 10.1109/TMC.2021.3053136 · DOI
  32. 10.1109/CC.2014.6911090 · DOI
  33. 10.1145/2909827.2930794 · DOI
  34. 10.1145/3447756 · DOI
  35. 10.1109/ICETCE48199.2020.9091779 · DOI
  36. 10.1109/ICCSE.2019.8845453 · DOI
  37. 10.1109/BigData.2018.8622140 · DOI
  38. 10.1109/IAEAC.2017.8054023 · DOI
  39. 10.1109/TIFS.2018.2840488 · DOI
  40. 10.1109/JSAC.2021.3088663 · DOI
  41. 10.1016/j.ins.2016.08.084 · DOI
  42. 10.1002/cpe.4415 · DOI
  43. 10.1109/ICNIDC.2018.8525664 · DOI
  44. 10.1109/ACCESS.2019.2957816 · DOI
  45. 10.1109/JIOT.2020.2963927 · DOI
  46. Fernando, A semantic similarity approach to paraphrase detection, 45
  47. 10.1109/ACCESS.2020.2981616 · DOI
  48. Miyato, Adversarial training methods for semi-supervised text classification, 61174
  49. 10.24963/ijcai.2018/601 · DOI

Metadata

Authors
Zhang, P., Wang, Y., Kumar, N., Jiang, C., & Shi, G.
Affiliations
  • Beijing University of Posts and Telecommunications (CN)
  • China University of Petroleum, East China (CN)
  • Asia University (TW)
  • Beijing National Research Center for Information Science and Technology (CN)
  • China Academy of Information and Communications Technology (CN)
ORCID
Venue
IEEE Transactions on Computational Social Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-02
Volume
9
Issue
1
Pages
97-108
ISSN
  • 2329-924X (ISSN-L)
  • 2373-7476
DOI
10.1109/tcss.2021.3092746
OpenAlex
W3182161186
Cited by
61
References
48
Citations by year
2026: 3 · 2025: 9 · 2024: 16 · 2023: 19 · 2022: 14
FWCI
4.33
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Computational Social Systems (publishedVersion)
Funders
  • National Key Research and Development Program of China (2020YFB1804800)
  • National Natural Science Foundation of China (61922050)
  • Major Scientific and Technological Projects of CNPC (ZD2019-183-006)
  • Shandong Provincial Natural Science Foundation, China (ZR2020MF006)
  • Open Foundation of State key Laboratory of Networking and Switching Technology (SKLNST-2021-1-17)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords
collaborative edge computing, social Internet of Things, privacy preservation, adversarial training, adversarial sample generation, firefly algorithm, deep learning robustness, sentence similarity, convolutional neural networks, data security

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review