Privacy-Preserving Framework Using Isolation Forest for Security

Federated Learning Frameworks for Privacy-Preserving Smart Applications

Internet of Things (IoT), edge computing, and cloud computing has transformed data-driven services across healthcare, transportation, manufacturing, finance, agriculture, and smart cities. These applications generate large volumes of sensitive distributed data, making traditional centralized machine learning unsuitable due to privacy, security, communication, and regulatory challenges. Federated Learning (FL) addresses these issues by enabling collaborative model training without transferring raw data, thereby preserving user privacy.This paper presents a privacy-preserving federated learning framework that integrates secure aggregation, differential privacy, encryption, adaptive communication, and federated optimization for large-scale heterogeneous environments. The framework incorporates edge computing, blockchain, Trusted Execution Environments (TEE), and Explainable AI (XAI) to improve security, transparency, and trust. A multi-layer architecture consisting of client devices, edge servers, federated coordinators, cloud services, and security modules is proposed. Adaptive client selection, weighted federated averaging, dynamic privacy allocation, and asynchronous synchronization are employed to improve learning under Non-IID data distributions. Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.

A Survey on Privacy-Preserving Techniques for Cloud Data Processing Using Homomorphic Encryption and Federated Learning

Cloud computing offers organizations scalable storage and computation, but outsourcing data processing to third-party infrastructure introduces serious privacy and confidentiality risks. Two complementary paradigms have emerged to address this challenge: homomorphic encryption (HE), which allows computation directly on encrypted data, and federated learning (FL), which enables collaborative model training without centralizing raw data. This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination. We propose a taxonomy of existing approaches, synthesize representative literature in a comparative table, illustrate a generic hybrid HE-FL architecture, and evaluate the two paradigms against criteria including data exposure, computational overhead, communication cost, resistance to inference attacks, and cloud deployment readiness. We further identify open challenges — including computational latency, key management, non-IID data distributions, and standardization gaps — and outline promising directions for future research, such as hardware-accelerated HE, adaptive encryption granularity, and standardized hybrid privacy frameworks for cloud-native machine learning.

A Federated Deep Learning Paradigm for Privacy-Preserving Cloud Applications

Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequently lack accuracy, scalability, or practicality in their operations. To overcome these limitations, a federated deep learning model is formulated so that secure joint learning can occur without transferring raw data across the domains of ownership. The framework incorporates training that is decentralized, training that uses differential privacy, training that uses secure aggregation, training that uses encrypted communication, and training that uses trust-based anomaly defense to defend against leakage, poisoning, and inference attacks. It also supports heterogeneous and highly non-IID datasets using adaptive coordination and stability-relevant participation regulation and meets emerging data protection requirements. The methods of resource-conscious orchestration and the optimization of communication eliminate overhead without obstructing the effectiveness of learning. The paradigm has therefore formed a privacy-by-design intelligent cloud ecosystem which ensures confidentiality, maintains performance, enhances robustness, and ensures responsible AI implementation in privacy-related sectors of healthcare, finance, governance, and smart infrastructure.

A Comprehensive Survey of AI Security Threats in IoT and Edge Cloud Systems

AI security is a key design criterion for IoT and edge cloud architectures where learning models are embedded near physical processes, receive streaming data from sensors, and are subject to continuous updates. This survey research work proposes a lifecycle-based understanding of AI security threats and proposed a unified taxonomy for the four major categories of threats observed in real-world settings, namely, data poisoning and backdoor attacks on learning model updates, adversarial attacks on model outputs through input manipulation, privacy leakage of confidential information through model-based queries, and model extraction for intellectual property theft and creation of rogue replicas of learning models. The research in surveying these defenses takes account of the limitations posed by the use of edge computing platforms from a computational standpoint, latency tolerance, network connections, and variety of hardware. These defense mechanisms include model provenance and data screening, robust training and backdoor attacks, monitoring and calibration, privacy-preserving learning and access control, and model protection through throttling, fingerprinting, watermarking, and attestation. Unlike prior surveys that treat these threats separately, this survey unifies them under a lifecycle-based taxonomy tailored to IoT and edge cloud deployments and emphasizes deployable defenses under latency, compute, and hardware constraints.

A Secure Federated Learning and Blockchain Framework for E-Health Threat Detection

An Intelligent Intrusion Detection and Privacy-Preserving Architecture for the Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) is transforming healthcare delivery, but brings significant security and privacy challenges due to the diverse range of devices, sensitive patient data, and real-time operation requirements. Existing Intrusion Detection Systems (IDS) have improved detection and privacy through approaches such as federated learning and blockchain, yet they focus primarily on network-level attacks, overlooking device-level attacks which is the primary source of data leakage, device unavailability, and model poisoning in federated learning (FL)-based approaches. For instance, Bring Your Own Device (BYOD) introduces heterogeneity and Non-Independent and Identically Distributed data (non-IID) distributions that affect the performance of conventional FL approaches. We therefore propose an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead. To preserve privacy and handle the issue of non-IID data across heterogeneous IoMT devices, we propose an adaptive FedProx-based weighted federated learning framework. Our proposed framework achieves an overall accuracy of 99.1% on the edge with latency between 1.8ms per sample, with a mean global accuracy of 99.4% and global loss of 0.037 during convergence, making it highly suitable for real-world IoMT deployments.