Prof. Nafei Zhu Profile

Prof. Nafei Zhu

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Proceedings Article | 22 August 2024 Paper

Machine-learning-based adaptive keyboard sound masking against acoustic channel attacks

Anastasiia Lobanova, Jingsha He, Nafei Zhu, Ahsan Nazir, Xiangjun Ma

Proceedings Volume 13228, 1322814 (2024) https://doi.org/10.1117/12.3038181

KEYWORDS: Defense and security, Acoustics, Gallium nitride, Machine learning, Signal generators, Acoustic waves, Matrices, Computer security, Background noise, Signal detection

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Keyboard acoustic side channel attacks exploit audio leakage from typing to deduce typed words with a certain level of accuracy. Researchers have been improving the accuracy of these attacks through various techniques of feature extraction and classification. They also apply machine learning methods and deep learning techniques to enhance the accuracy of their results. At the same time, defense mechanisms against these attacks have not kept up with the increasing precision of the attacks. In this study, we introduce a practical defense strategy against keyboard acoustic attacks during password and text typing. We evaluate its effectiveness against multiple attack vectors. Our defense strategy involves generating unique background sounds using GANs to mask sensitive audio leaks from the keyboard, thwarting side channel attacks from extracting usable information about typed content. The background sounds are produced by the device used for text input. We assess the usability of our approach for short and prolonged usage durations, demonstrating that the addition of background sounds does not impede users’ ability to enter passwords or perform computer tasks effectively.

Proceedings Article | 26 May 2023 Paper

A vector compression coding algorithm based on SIMD(VCABSIMD)

Hu Wang, Jingsha He, Nafei Zhu, Haiyang He

Proceedings Volume 12700, 127001Y (2023) https://doi.org/10.1117/12.2682252

KEYWORDS: Computing systems, Mathematical optimization, Clouds, Binary data, Machine learning, Image encryption, Computer security, Visualization, Switching, Mathematical modeling

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In the vector operation system, each data operation will only increase or decrease the same number for each component of the vector, that is to say, the vector is regarded as a data object on the whole, and each operation is carried out on the whole. For example, translation, scaling and other operations in computer graphics. Such computing systems are currently widely used in graphics, machine learning, mathematical modeling and other fields. When the data in the vector computing system is homomorphic encrypted and uploaded to the cloud server, the problem of low vector coding efficiency and polynomial coefficient expansion caused by a large number of polynomial operations is generated. The existing SIMD coding algorithm encodes an n-dimensional vector integer into a polynomial. The number of calculations increases exponentially with the increase of n, and the coding efficiency of the vector is very low. For typical FHE schemes such as BGV, BFV and CKKS, the main performance bottleneck comes from a large number of polynomial algorithms. Specifically, encrypted data is typically composed of a pair of polynomials with coefficients of hundreds or thousands of bits, requiring expensive multiword arithmetic. In addition, large polynomial lengths increase the computational complexity. Therefore, in this paper, a vector compression coding algorithm based on SIMD(VCABSIMD) is proposed to solve the performance problem when the data in the vector computing system is homomorphically encrypted and uploaded to the cloud server for calculation. The vector coding efficiency and polynomial calculation are deeply optimized.

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