Privacy-Preserving Swarm Learning Based on Homomorphic Encryption

Chen, Lijie; Fu, Shaojing; Liu, Lin; Luo, Yuchuan; Zhao, Wentao

doi:10.1007/978-3-030-95391-1_32

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Recently, researchers have focused on reducing the cost of computing cryptographic operations in conventional PPDA systems [16]. By anticipating the demand for electricity in a smart grid system, a light and secure system was proposed.…”

Section: Related Workmentioning

confidence: 99%

ES-PPDA: an efficient and secure privacy-protected data aggregation scheme in the IoT with an edge-based XaaS architecture

Chen

Jiang

2022

J Cloud Comp

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In an Internet of Things (IoT) system based on an anything as a service (XaaS) architecture, data are uploaded from heterogeneous nodes in a nonstandardized format and aggregated on the server side. Within this data-intensive architecture, privacy preservation is one of the most important issues. In response to this concern, there are numerous privacy-protection data aggregation (PPDA) solutions available for various IoT applications. Because of the limited resources of intelligent IoT devices, traditional PPDA cannot meet practical privacy and performance needs. To tackle this challenge, we provide a more efficient and secure PPDA solution that guarantees data security and integrity through Paillier homomorphic encryption and online/offline signing technology. Detailed security analysis shows that our system is unpredictable under a chosen message attack, and the data integrity may be guaranteed under the assumption of q-strong Diffie-Hellman (Q-SDH). We choose an M/G/1 priority queue model to maximize system performance. M/G/1 enhances queuing efficiency and accelerates channel access, thus reducing waiting time and increasing reliability. The experimental results show that our data aggregation scheme is reliable with low latency.

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Section: Related Workmentioning

confidence: 99%