Performance evaluation of differential privacy mechanisms in blockchain-based smart metering

In this chapter, the author first work over the integration of differential privacy and blockchain in real-time smart metering scenario. Afterwards, we carried out performance evaluation of four variants of differential privacy in the proposed blockchain-based smart metering scenario. The performance evaluation section of this chapter demonstrates that each privacy -preserving mechanism adds differently depending upon the selected privacy parameters and the input data. However, in the case of high peak values the Geometric mechanism surpasses other noise addition variants, although, in case of low peak values in metre reading, the Laplace mechanism performs better at e = 0.01. As a part of future work, we are working over the integration of differential privacy and blockchain in other cyber-physical systems scenarios. Chapter Contents: • 9.1 Introduction • 9.1.1 Key contributions • 9.1.2 Related work • 9.2 Preliminaries of our work • 9.2.1 Differential privacy mechanisms • 9.2.1.1 Laplace mechanism • 9.2.1.2 Gaussian mechanism • 9.2.1.3 Uniform mechanism • 9.2.1.4 Geometric mechanism • 9.2.2 Real-time smart metering and privacy issues • 9.2.3 Blockchain network • 9.3 Functioning and system model • 9.3.1 System model • 9.3.2 Design goals • 9.3.3 Adversary model • 9.3.4 Algorithmic foundation • 9.4 Performance evaluation • 9.4.1 Simulation parameters • 9.4.2 Private real-time data reporting • 9.4.3 Mean absolute error • 9.4.4 Summary and lessons learnt • 9.5 Conclusion and future directions • References.