A Blockchain-Assisted Adaptive Differential Privacy Framework for Secure, Privacy-Preserving, and Tamper-Proof Cloud Data Management
TL;DR
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.
Abstract
Cloud computing has become the backbone of modern distributed data management, offering scalable storage and on-demand analytics to healthcare, financial, educational, and government organizations. However, two security requirements are frequently addressed in isolation in the literature: preserving the privacy of sensitive attributes during analytical processing, and guaranteeing that outsourced data remains unmodified and verifiable while it resides with an untrusted cloud service provider. This paper analyzes two complementary cloud-security paradigms adaptive differential privacy for perturbation-based confidentiality and blockchain-assisted third-party auditing for tamper-evident integrity verification and proposes a unified Blockchain-Assisted Adaptive Differential Privacy (BA-ADP) framework that integrates both into a single secure cloud data-management pipeline. The proposed framework performs sensitivity-aware adaptive noise generation to protect data confidentiality during analytics, applies lightweight encryption and access control during storage and transmission, and simultaneously anchors SHA-256 integrity hashes of every data block on a blockchain ledger that is verified through smart-contract-automated third-party auditing. This dual-layer design allows a cloud system to resist inference, linkage, and reconstruction attacks on the privacy side while resisting tampering, single-point-of-failure, and collusion risks on the integrity side, without requiring two independent security subsystems. Drawing on previously reported component-level results 96.1% privacy-protection efficiency with 3.8% leakage probability for the adaptive differential-privacy layer, and 97.4% integrity-verification accuracy with a 96.8% tampering-detection rate for the blockchain-assisted auditing layer this paper presents an architectural synthesis, a security analysis, and a discussion of the expected computational trade-offs of combining both mechanisms. 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.