Real-Time Privacy-Preserving Analytics for Smart Grids using Levelled Homomorphic Encryption
| dc.contributor.author | Irengbam, Preceela Chanu | |
| dc.date.accessioned | 2026-09-23T06:12:02Z | |
| dc.date.available | 2026-09-23T06:12:02Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | This thesis presents a privacy-preserving smart grid analytics framework that combines realistic energy consumption modeling with end-to-end encrypted aggregation using CKKS homomorphic encryption. The proposed system implements a three-tier architecture comprising smart meters, an aggregator, and a control center, supported by a certificate authority and a key distribution center for secure TLS 1.3 communication and mutual X.509 authentication. Smart meters simulated household load profiles across low, medium, and variable consumer classes, generating realistic 24-hour demand curves with peak and trough behavior, day-of-week variation, and a low probability of zero readings. Meter readings are encrypted at the source and transmitted securely to an aggregator that performs batched homomorphic additions on cipher texts without accessing plain text data. The control center with the secret key decrypts aggregated results. It produces analytics reports, including homomorphic operation costs, encryp-tion performance,scalibility and network latency. | |
| dc.identifier.uri | http://nits.ndl.gov.in/handle/123456789/121 | |
| dc.language.iso | en | |
| dc.publisher | National Institute of Technology, Silchar | |
| dc.title | Real-Time Privacy-Preserving Analytics for Smart Grids using Levelled Homomorphic Encryption |
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