Title: Cloud computing's multi-key privacy-preserving deep learning system

Authors: A. Mani; M. Shanmuganathan; R. Babitha Lincy; J. Jency Rubia

Addresses: Department of Computer Science and Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India ' Dept of C.S.E., Panimalar Engineering College, Chennai-600123, Tamil Nadu, India ' Computer and Communication Engineering Department, Sri Eshwar College of Engineering, Kinathukadavu, Coimbatore 641005, India ' Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India

Abstract: Many fields have seen success with deep learning implementations, including bioinformatics, photo processing, gaming, computer security, etc. However, a large amount of training data is typically required for deep learning, which may not be made available by a single owner. As the amount of data continues to rise at an exponential rate, many people are turning to remote cloud services to store their information. Human activity recognition (HAR) provides massive amounts of data from IoT devices to collaboratively construct predictive models for medical diagnosis. To protect users' anonymity in scenarios where DNNs are used in HAR learning, we present Multi-Scheme Differential Privacy. MSDP uses a multi-party, secure variant of the ReLU function to cut down on transmission and processing time. MSDP is proved to be secure in comparison to existing state-of-the-art models without compromising privacy through experimental validation on four of the most popular human activity detection datasets.

Keywords: internet of things; IoT; multi-key privacy-preserving; deep learning.

DOI: 10.1504/IJESDF.2025.148237

International Journal of Electronic Security and Digital Forensics, 2025 Vol.17 No.5, pp.604 - 615

Received: 18 Jul 2023
Accepted: 21 Dec 2023
Published online: 01 Sep 2025 *

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