arXiv subjects

Yanmin Zhao

Publications and source records attributed to Yanmin Zhao.

3 recordsLinked to original sources

arXiv2024

Cyber-Physical Authentication Scheme for Secure V2G Transactions

The rapid adoption of electric vehicles (EVs) globally has catalyzed the need for robust cybersecurity measures within vehicle-to-grid (V2G) networks. As these networks are increasingly being integrated into smart charging infrastructures, they also introduce new vulnerabilities that threaten grid stability and user privacy This paper proposes a cyber-physical authentication protocol and trading smart contract tailored to plug and charge (PnC) operations within blockchain-based V2G systems. The protocol leverages advanced cryptographic techniques and blockchain to ensure secure, transparent, and tamper-proof energy transactions between EVs and charging stations. Key contributions include the development of a cyber-physical authentication method, the implementation of a smart contract framework for secure energy trading, and a detailed security and privacy analysis. The proposed protocol effectively mitigates risks such as man-in-the-middle (MitM) attacks and replay attacks while preserving user anonymity and data integrity.

cs.CR↗

arXiv2022

SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE

In recent years, distributed machine learning has garnered significant attention. However, privacy continues to be an unresolved issue within this field. Multi-key homomorphic encryption over torus (MKTFHE) is one of the promising candidates for addressing this concern. Nevertheless, there may be security risks in the decryption of MKTFHE. Moreover, to our best known, the latest works about MKTFHE only support Boolean operation and linear operation which cannot directly compute the non-linear function like Sigmoid. Therefore, it is still hard to perform common machine learning such as logistic regression and neural networks in high performance. In this paper, we first discover a possible attack on the existing distributed decryption protocol for MKTFHE and subsequently introduce secret sharing to propose a securer one. Next, we design a new MKTFHE-friendly activation function via \emph{homogenizer} and \emph{compare quads}. Finally, we utilize them to implement logistic regression and neural network training in MKTFHE. Comparing the efficiency and accuracy between using Taylor polynomials of Sigmoid and our proposed function as an activation function, the experiments show that the efficiency of our function is 10 times higher than using 7-order Taylor polynomials straightly and the accuracy of the training model is similar to using a high-order polynomial as an activation function scheme.

cs.CR↗

arXiv2022

A unconditionally energy dissipative, adaptive IMEX BDF2 scheme and its error estimates for Cahn-Hilliard equation on generalized SAV approach

An adaptive implicit-explicit (IMEX) BDF2 scheme is investigated on generalized SAV approach for the Cahn-Hilliard equation by combining with Fourier spectral method in space. It is proved that the modified energy dissipation law is unconditionally preserved at discrete levels. Under a mild ratio restriction, i.e., \Ass{1}: $0<r_k:=τ_k/τ_{k-1}< r_{\max}\approx 4.8645$, we establish a rigorous error estimate in $H^1$-norm and achieve optimal second-order accuracy in time. The proof involves the tools of discrete orthogonal convolution (DOC) kernels and inequality zoom. It is worth noting that the presented adaptive time-step scheme only requires solving one linear system with constant coefficients at each time step. In our analysis, the first-consistent BDF1 for the first step does not bring the order reduction in $H^1$-norm. The $H^1$ bound of the numerical solution under periodic boundary conditions can be derived without any restriction (such as zero mean of the initial data). Finally, numerical examples are provided to verify our theoretical analysis and the algorithm efficiency.

math.NA↗