2024/08/16 by Altaf Hussain, Wajahat Akbar, Tariq Hussain +4 · 1 citation
Computer Science · Social Sciences · #Blockchain #Blockchain Technology Applications and Security #Computer science #Computer security #Cryptography #Data mining #Differential privacy #Information privacy #Internet of Things #Internet privacy #Privacy protection #Privacy software #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Zero (linguistics) #Zero-knowledge proof
paper · doi:10.1109/tce.2024.3444824
openalex publication_date 2024/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and l-diversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%.