Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 1043-1046· 0 citations· 11 references
Abstract
As intelligent home services increasingly adopt IoT-Edge-Cloud architectures, protecting personal data during third-party data sharing has become an important challenge. This paper presents a Service-Aware Trust Chain framework that applies Privacy-Enhancing Technologies (PETs) according to data sensitivity. To support this approach, we introduce a 7-level security grading scheme based on Korea’s Personal Information Protection Act (PIPA) and practical deployment requirements in edge environments.A proof-of-concept system was implemented on a Raspberry Pi 5, integrating five privacy protection engines: ARX-based deidentification, Clusion searchable encryption, Microsoft SEAL homomorphic encryption, video face blurring, and sound masking. Performance measurements show that ARX and searchable encryption can be executed with low latency at the edge, whereas video and audio protection mechanisms are more suitable for batch-oriented processing. Homomorphic encryption incurs substantially higher overhead and is therefore better suited to a cloud-assisted deployment model. In addition, sound masking exhibited limited performance degradation as audio length increased, indicating its practicality for voice privacy protection in smart-home environments.To support data integrity verification, only cryptographic hashes are recorded on a cloud-hosted Trust Chain, avoiding the exposure of raw data. The proposed framework also outlines future extensions including sLLM-based data classification and DID-based access control.
A privacy-preserving, secure data-sharing framework tailored for edge-cloud collaborative architectures that minimizes the computational overhead on the terminal side while safeguarding user privacy, and effectively reduces the overhead associated with user joining and revocation within the same group.
Qikun Zhang, Zheng Cai, Jinbo Feng et al.· Journal of King Saud Univers...· 0 citations
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, and identifies promising directions for future research.
Shivendra Shukla, C. S. Gautam, Divyansh Tiwari· International Journal of Cre...· 0 citations
Data management must not be confined to existing cloud infrastructure but should be extended, considering the ever-increasing volume of digital data. As such, the proposed research introduces an innovative framework dubbed Fog-Integrated Distributed Cloud Optimization that makes use of distributed servers from the local fog as a supplementary means to provide increased performance and enhanced security. The FIDCO framework will provide a means of seamless connectivity between the fog servers and the primary cloud storage to achieve optimal data management using secure measures. Some of the contributions include efficient data management, whereby a system with minimal latency and data migration for data integrity is designed using a scalable distributed architecture. Ensuring a secure data transfer protocol can be done by implementing end-to-end encryption and real-time monitoring for any security threats, such as DDoS attacks. Methodologies adopted will make use of simulation-based modeling and prototyping implementation to evaluate the efficiency of the FIDCO framework concerning its capacity to secure high-performance data management. Today's development stage involves real-time threat detection and appropriate response to any threats that occur, leading to improved data integrity, hence the current evolution of the distributed cloud architecture known as FIDCO.
K. Sindhuja, D. Benitta· international journal of eng...· 0 citations
As smart cities increasingly rely on IoT-enabled surveillance for public safety, the industry-standard practice of streaming raw footage to centralized cloud servers has introduced critical vulnerabilities regarding data privacy and accountability. Current "collect-first, protect-later" architectures create massive targets for cyberattacks and unauthorized administrative access. This report will propose an exceptional, three-layer security framework which consists of the Edge Layer, the Cloud Layer, and the Access Layer. Our particular design is to introduce a "Privacy-by-Design" methodology. At the Edge Layer, we implement an atomic, real-time anonymization process using lightweight deep learning models like YOLO (You Only Look Once), BlazeFace, and Reversible Chaotic Masking. This ensures that Personally Identifiable Information (PII) is redacted in ephemeral memory before network transmission, effectively neutralizing Man-in-the-Middle attacks. The Cloud Layer secures data that is not being transmitted via AES-256-GCM and ensures model integrity through OpenSSF Model Signing. Crucially, the Access Layer addresses the risk of data breaches and unauthorized access by utilizing Role-Based Access Control (RBAC) with a blockchain-based immutable audit ledger. To address hardware constraints, like a device with outdated hardware, this system utilizes Particle Swarm Optimization (PSO) for intelligent task offloading. After extensive comparisons, we can confirm that this holistic approach offers superior privacy protection, bandwidth efficiency, and forensic non-repudiation compared to other existing centralized surveillance models.
Wong Leong Hin, Kyle Adam Frank, Kosei Yamashita et al.· International Journal of Eme...· 0 citations
The consumer Internet of Things (CIoT) enables large-scale sensing and data-driven services by connecting billions of devices. However, the privacy-preserving data aggregation in mobile edge computing (MEC)-enabled CIoT remains challenging when the edge aggregator is malicious, rather than simply honest-but-curious. Specifically, a malicious MEC server can selectively drop user reports or tamper with ciphertexts, undermining data integrity and ownership. To address this, we propose a verifiable and fault-tolerant privacy-preserving aggregation scheme. By substituting computationally intensive homomorphic encryption (HE) with a double-masking mechanism and integrating bilinear pairing-based accumulators, the proposed scheme provides resilience against tampering and deletion attacks while ensuring data privacy. The design supports user dropouts within a single reporting round without requiring interactive recovery, enabling each device to locally audit the inclusion of the corresponding ciphertext via a compact membership witness. Theoretical analysis and experimental results demonstrate that the proposed scheme reduces communication overhead and payload size. It satisfies the security requirements of CIoT ecosystems and offers a practical tradeoff between security and efficiency for latency-sensitive deployments.
Jianhong Zhang, Miao Xue, Xinyu Bai· IEEE Internet of Things Jour...· 0 citations
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