Aug 2026· International Journal of Creative and Open Research in Engineering and Management· 0 citations
TL;DR
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.
Abstract
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 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
A viable, privacy-friendly auditing framework of clouds which guarantees the end-toend encrypted verification without sacrificing the efficiency is presented.
Deepshikha Chaturvedi, Vidyullata Devmane, Shashikant S. Radke et al.· International Journal of Com...· 0 citations
The findings indicate that unifying adaptive privacy preservation with decentralized integrity auditing yields a more complete cloud-security posture than either mechanism alone, and the paper outlines the empirical validation, including full-scale testbed experiments, required before deployment.
Jayakumar D, M. Ramamoorthy· International journal of com...· 0 citations
This paper presents a secure data sharing platform that organises KR-IBI, KR-IBE, KR-PEKS, and KR-PAEKS into an end-to-end Rust/Tauri workflow for registration, authentication, encrypted upload, searchable retrieval, and authorised decryption. The work addresses a deployment-level composition problem rather than proposing a new primitive: practical data sharing requires coordinated credential handling, payload representation, searchable indexing, session control, and record integrity across schemes with distinct interfaces. The platform supports text, file, and image payloads through a hybrid KR-IBE/HKDF-SHA-256/AES-256-GCM layer. Fresh KR-IBE key material is generated by uniformly sampling a nonzero scalar and multiplying the Ed25519 prime-order subgroup generator, providing approximately 252 bits of min-entropy before HKDF derivation. An evaluation with 100 repetitions per configuration over Enron-derived workloads containing 100–10,000 records and 1, 5, 10, or 20 authorised identities achieved 100/100 correctness for authorised retrieval and decryption, wrong-keyword and wrong-scheme rejection, and unauthorised-access rejection. KR-PEKS search latency ranged from 29.26 ms at 100 records to 3023.82 ms at 10,000 records, whereas KR-PAEKS ranged from 775.53 ms to 93,045.52 ms. These results quantify the performance distinction between the lower-latency KR-PEKS mode and the sender-authenticated searchable encryption provided by KR-PAEKS.
The increasing needs in data sharing in the fields of finance, governance, and artificial intelligence pose a major threat to privacy, particularly in quantum computing. In this paper, a hybrid privacy-preserving system incorporating simulated BB84 Quantum Key Distribution (QKD) to generate secure keys, reversible pseudonymization with encrypted mapping vaults, automatic key rotation, and re-identification risk analysis by machine learning are introduced. Also, optional differential privacy layer allows irreversible anonymization in the cases of analysis. The proposed system will enable two modes, that is, recovery of secure data and the ability to publish data in privacy modes. The experimental findings indicate that authorized users have 100% recovery accuracy, re-identification risk is low and is close to random guessing and data utility is acceptable given the privacy restrictions. FastAPI and Streamlit are used to implement the framework, which is appropriate in the real-world deployment in clouds.
Srividhya Ganesan, G. Vijayasekaran, S. R.· 2026 4th International Confe...· 0 citations
Federated learning is a technology that is used to protect data privacy in machine learning. Nonetheless, in federated learning, updating the global model requires the use of gradient descent algorithm, which involves multiple rounds of interaction between entities to complete the iterative updates, inevitably incurring massive computational and communication overhead. In 2020, Wang et al. first proposed a non-interactive federated regression scheme, which effectively improves the training efficiency of regression models while protecting the privacy of local training data. However, like most current federated regressions, it involves a third authority (TA) to generate keys for each entity, which poses a significant privacy risk and results in considerable communication overhead. From the view of security and practicality, this paper first proposes a multi-party homomorphic encryption algorithm named MPaillier. Furthermore, we have designed PNFR, a privacy-preserving federated learning scheme for regressions training built on the MPaillier algorithm. The participating entities of PNFR are the data owners and a cloud server, eliminating the need for a TA, thus enhancing the practicality and efficiency of the scheme. Experimental results demonstrate that our scheme is $\sim 10^{3}$ times faster than interactive federated regressions PrivFL and about 80% faster than non-interactive federated regressions VANE.
