mlsanityData to 7 Oct 2026

Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems

arXiv 2109.14334 ↗First posted 29 Sep 2021cs.CR cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Recently, many innovations have been experienced in healthcare by rapidly growing Internet-of-Things (IoT) technology that provides significant developments and facilities in the health sector and improves daily human life. The IoT bridges people, information technology and speed up shopping. For these reasons, IoT technology has started to be used on a large scale. Thanks to the use of IoT technology in health services, chronic disease monitoring, health monitoring, rapid intervention, early diagnosis and treatment, etc. facilitates the delivery of health services. However, the data transferred to the digital environment pose a threat of privacy leakage. Unauthorized persons have used them, and there have been malicious attacks on the health and privacy of individuals. In this study, it is aimed to propose a model to handle the privacy problems based on federated learning. Besides, we apply secure multi party computation. Our proposed model presents an extensive privacy and data analysis and achieve high performance.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 29 Sep 2021.

Builds on

14 of 39 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019Towards Federated Learning at Scale: System Designcited by 826
  4. 2020Federated Learning with Matched Averagingcited by 564
  5. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  6. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  7. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  8. 2019Peer-to-peer Federated Learning on Graphscited by 85
  9. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  10. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 73
  11. 2019MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Samplingcited by 67
  12. 2020Heterogeneous Federated Learningcited by 19
2 more
  1. 2020Hierarchical Federated Learning through LAN-WAN Orchestrationcited by 13
  2. 2020Data Augmentation Based Malware Detection using Convolutional Neural Networkscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  2. 2022Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Casecited by 11
  3. 2026PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoTcited by 0