# Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection

arXiv: https://arxiv.org/abs/2304.10833 · HTML: https://mlsanity.com/paper/2304.10833/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2023.

## Built on by (most cited first, 7 of 7)

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- [A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis](https://mlsanity.com/paper/2311.01842/) (2023)
- [IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support](https://mlsanity.com/paper/2311.04258/) (2023)
- [Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm](https://mlsanity.com/paper/2311.11520/) (2023)
- [UNCA: A Neutrosophic-Based Framework for Robust Clustering and Enhanced Data Interpretation](https://mlsanity.com/paper/2502.17523/) (2025)

## Abstract (arXiv)

Huge diagrams have unique properties for organizations and research, such as client linkages in informal organizations and customer evaluation lattices in social channels. They necessitate a lot of financial assets to maintain because they are large and frequently continue to expand. Owners of large diagrams may need to use cloud resources due to the extensive arrangement of open cloud resources to increase capacity and computation flexibility. However, the cloud's accountability and protection of schematics have become a significant issue. In this study, we consider calculations for security savings for essential graph examination practices: schematic extraterrestrial examination for outsourcing graphs in the cloud server. We create the security-protecting variants of the two proposed Eigen decay computations. They are using two cryptographic algorithms: additional substance homomorphic encryption (ASHE) strategies and some degree homomorphic encryption (SDHE) methods. Inadequate networks also feature a distinctively confidential info adaptation convention to allow the trade-off between secrecy and data sparseness. Both dense and sparse structures are investigated. According to test results, calculations with sparse encoding can drastically reduce information. SDHE-based strategies have reduced computing time, while ASHE-based methods have reduced stockpiling expenses.