skip to main content
10.1145/3475992.3476003acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbiotcConference Proceedingsconference-collections
research-article

Secure, Decentralized, Privacy Preserving Machine Learning System Implementation over Blockchain

Published: 02 October 2021 Publication History

Abstract

The traditional approach to centralized machine learning has transparency concerns. The future of machine learning is decentralized machine learning. Thus, many technological advance companies including Microsoft are also investing in researching approaches to decentralization in machine learning. With the upliftment of big data technology, designing optimized artificial intelligence algorithms is a must need. At the base of every machine learning algorithm we need data. Data is something that can be any unprocessed fact, value, text, sound or picture that is not being interpreted and analyzed. This data is not generated by just one party, multiple parties generate such data. The data will be geographically distributed amongst organizations. This pushes the need and research of distributed machine learning algorithms. In the current scenario, there is a central server which will run the machine learning algorithm and produce results, in this system obviously we need to collect all the data at that server itself. If the server is attacked then there is a problem of security of data. Also many organizations would not like to just lend their data to some third party. To address all such issues, we study all the possible ways for implementing a distributed machine learning system and propose a blockchain based distributed conservative system. Mainly, we design a Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the trending blockchain technology, also taking care of Byzantine attack, using the within-N algorithm. Also analysis will be made on different machine learning algorithms and datasets as a part of testing, demonstrating the effectiveness of the model.

References

[1]
Xuhui Chen, Jinlong Ji, Changqing Luo, Weixian Liao and Pan Li, “When Machine Learning Meets Blockchain: A Decentralized, Privacy-preserving and Secure Design” in 2018 IEEE International Conference on Big Data
[2]
J. Hamm, Y. Cao, and M. Belkin, “Learning privately from multiparty data,” in International Conference on Machine Learning, 2016, pp. 555– 563
[3]
A. Rajkumar and S. Agarwal, “A differentially private stochastic gradient descent algorithm for multiparty classification,” in Artificial Intelligence and Statistics, 2012, pp. 933–941.
[4]
Suhel Sayyad, “Privacy Preserving Deep Learning using Secure Multiparty Computation”, in 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)
[5]
R. J. McQueen, S. R. Garner, C. G. Nevill-Manning, and I. H. Wit-ten, “Applying machine learning to agricultural data,” Computers and electronics in agriculture, vol.12, no. 4, pp. 275–293, 1995.
[6]
X. Chen, J. Ji, T. Ji, and P. Li, “Cost-sensitive deep active learning for epileptic seizure detection,” in Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2018, pp. 226–235.
[7]
X. Chen, J. Ji, K. Loparo, and P. Li, “Real-time personalized cardiac arrhythmia detection and diagnosis: A cloud computing architecture,” in Biomedical Health Informatics (BHI), 2017 IEEE EMBS International Conference on. IEEE, 2017, pp.201–204.
[8]
C. Luo, J. Ji, Q. Wang, X. Chen, and P. Li, “Channel state information prediction for 5g wireless communications: A deep learning approach,”IEEE Transactions on Network Science and Engineering, 2018.
[9]
M. L. de Prado, Advances in financial machine learning. John Wiley Sons, 2018.
[10]
P. Blanchard, R. Guerraoui, J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in Advances in Neural Information Processing Systems, 2017, pp. 119–129.
[11]
C. Xie, O. Koyejo, and I. Gupta, “Zeno: Byzantine-suspicious stochastic gradient descent,” arXiv preprint arXiv:1805.10032, 2018.
[12]
C. Xie, O. Koyejo, and I. Gupta, “Generalized byzantine-tolerant sgd,” arXiv preprint arXiv:1802.10116, 2018.

Cited By

View all
  • (2022)A Novel Framework for Secure Blockchain Transactions2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)10.1109/ICAAIC53929.2022.9792758(1311-1318)Online publication date: 9-May-2022

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
BIOTC '21: Proceedings of the 2021 3rd Blockchain and Internet of Things Conference
July 2021
82 pages
ISBN:9781450389518
DOI:10.1145/3475992
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 October 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Decentralization
  2. Gradient Descent
  3. Privacy
  4. Security

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

BIOTC 2021

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)19
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2022)A Novel Framework for Secure Blockchain Transactions2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)10.1109/ICAAIC53929.2022.9792758(1311-1318)Online publication date: 9-May-2022

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media