Regional Load Forecasting Scheme for Security Outsourcing Computation
Abstract
:1. Introduction
- In the context of smart grids, this paper introduces an outsourced model secure-training protocol alongside a secure online prediction protocol. Both protocols seamlessly integrate a range of privacy preservation techniques while optimizing computational performance in a balanced manner. Specifically, during the backpropagation phase of the training protocol, the differential privacy algorithm is employed to safeguard gradients, thereby circumventing the need for time-intensive homomorphic ciphertext multiplication and enhancing overall efficiency.
- The secure outsourced computing scheme presented in this paper necessitates the participation of merely one cloud server in protocol interactions. It confines the computational process to just the data holder and a solitary outsourced computing entity, thereby mitigating the overhead arising from interactions among multiple, non-colluding servers. This optimization renders the scheme significantly more practical for real-world implementations.
2. Related Work
3. System Model
- Cloud Server (CS): A reputable cloud service provider’s cloud server possesses impressive computing, storage, and communication capabilities. This server is equipped to acquire deep learning models through online training and capture real-time power load variations through instantaneous online prediction. Consequently, smart grids can utilize these predictive outcomes for efficient power management and scheduling.
- Smart Grid Control Center (SGCC): The smart grid control center is responsible for formulating power scheduling strategies and scheduling power resources in the smart grid. It aggregates load data from different regions. In our system, the SGCC outsources computational tasks to specialized cloud service providers, utilizes models built by cloud service providers, and employs their load prediction services for outsourced load forecasting.
- Power Distribution (PD): The power distribution network is the infrastructure responsible for power distribution. It primarily bears two responsibilities: collecting power data from regional power grids and aggregating them to the SGCC and transmitting control commands from the SGCC to the smart grid.
- Neighborhood IoT Grid (NIG1, NIG2, …, NIGn): Smart grid household users are divided into multiple neighborhood IoT grids. The smart grid supplies power to each neighborhood IoT grid while being responsible for regulating the entire power system. Each neighborhood IoT grid will generate a large amount of regional power data, which will be uploaded and stored in the smart grid system.
4. Our Scheme
4.1. Smart Grid Security Outsourcing Model Training Protocol
4.1.1. System Initialization
4.1.2. Data Encryption
4.1.3. Forward Propagation
Algorithm 1 Privacy-Preserving Forward Propagation Algorithm. |
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4.1.4. Backward Propagation
4.2. Smart Grid Security Outsourcing Online Forecasting Protocol
Algorithm 2 Privacy-Preserving Backpropagation Algorithm. |
Require: Forward propagation output u, encrypted true values y, learning rate
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4.2.1. Encryption Processing
4.2.2. Server-Side Computing
4.2.3. Decryption Processing
5. Security Analysis
6. Experiments
6.1. Experiment Settings
6.1.1. DataSet
6.1.2. Experimental Environment
6.1.3. Neural Network Structure
6.2. Training Protocol Performance Analysis
6.2.1. Comparison with the Existing Scheme
6.2.2. Privacy Analysis
6.3. Predictive Protocol Performance Analysis
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Pure-HE | SecureNN | |
---|---|---|
Server Count | 1 | 3 |
Anti-Collusion | ✓ | ✓ |
Activation Function | Table-Lookup | Table-Lookup |
Technologies Used | HE | SS, HE, DP |
Model | Dataset | Training Time (s) |
---|---|---|
Pure-HE | PJM | 7328.34 |
SecureNN | PJM | 612.32 |
Secure Outsourcing Training Scheme | PJM | 1811.02 |
Pure-HE | GEFCom2012 | 36,091.72 |
SecureNN | GEFCom2012 | 2613.98 |
Secure Outsourcing Training Scheme | GEFCom2012 | 9668.91 |
Privacy Level () | MAPE (%) | Utility (%) |
---|---|---|
No Noise | 2.03 | 100 |
2.46 | 99.52 | |
2.85 | 99.08 | |
3.15 | 98.91 | |
3.49 | 98.57 | |
3.83 | 98.19 | |
6.50 | 95.34 | |
15.91 | 86.39 |
Dataset | Pure-HE (s) | ABY3 (s) | Proposed Secure Prediction Protocol (s) |
---|---|---|---|
PIM | 158.634 | 14.183 | 42.842 |
GEFCom2012 | 147.627 | 13.091 | 40.915 |
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Chen, Q.; Zhao, R.; Li, B.; Liu, Z.; Zhuang, H.; Hu, C. Regional Load Forecasting Scheme for Security Outsourcing Computation. Electronics 2024, 13, 3712. https://doi.org/10.3390/electronics13183712
Chen Q, Zhao R, Li B, Liu Z, Zhuang H, Hu C. Regional Load Forecasting Scheme for Security Outsourcing Computation. Electronics. 2024; 13(18):3712. https://doi.org/10.3390/electronics13183712
Chicago/Turabian StyleChen, Qizhan, Ruifeng Zhao, Bin Li, Zewei Liu, Huijun Zhuang, and Chunqiang Hu. 2024. "Regional Load Forecasting Scheme for Security Outsourcing Computation" Electronics 13, no. 18: 3712. https://doi.org/10.3390/electronics13183712
APA StyleChen, Q., Zhao, R., Li, B., Liu, Z., Zhuang, H., & Hu, C. (2024). Regional Load Forecasting Scheme for Security Outsourcing Computation. Electronics, 13(18), 3712. https://doi.org/10.3390/electronics13183712