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Fenghua Li

6 accepted papers

2025

Accurate Hardware Trojan Detection for SGIN Device: A Prompt-Tuning and LangChain Approach

ICASSP 2025accepted

Space-Ground Integrated Networks (SGIN) devices are at risk of hardware Trojan attacks. Currently, existing detection schemes (e.g., deep learning) require a large amount of labeled samples. However, obtaining a high-quality labeled hardware Trojan dataset for SGIN devices is challenging due to the…

Cited by 0SourceScholar
2025

Dynamically Optimize MTD Strategy in Satellite Computing Systems Using A2C Reinforcement Learning

ICASSP 2025accepted

The Satellite Computing System (SCS) faces an increasing number of attacks. Although Moving Target Defense (MTD) can effectively mitigate attacks in ground networks, it is not well-suited for SCS due to the highly dynamic nature of both SCS traffic and attackers’ scanning behaviors. In this paper, w…

Cited by 0SourceScholar
2025

SEHAP: Secure and Efficient Handover Authentication Protocol in LEO Satellite Non-Terrestrial Networks

ICASSP 2025accepted

LEO satellite non-terrestrial networks (NTN) utilize satellites in Low Earth Orbit (LEO) to dynamically establish global communication service and own significant promise. The dynamic nature of LEO satellite NTN necessities efficient handover authentication protocols. However existing schemes cannot…

Cited by 0SourceScholar
2025

Toward Forward-Secure End-to-End Data Sharing: An Attribute-Key-Free CP-ABE Scheme

ICASSP 2025accepted

In end-to-end data sharing, data are directly distributed to data receivers and stored on their terminals, making it hard to ensure forward security because receivers whose permissions have been revoked may still access previously shared data. To address these challenges, we propose an attribute-key…

Cited by 0SourceScholar
2024

Interpreting Memorization in Deep Learning from Data Distribution

ICASSP 2024accepted

A deep learning model can be vulnerable to a membership inference attack (MIA) which allows an attacker to determine if a specific data record was used for its training. In this paper, we investigate the unfairness of disparate vulnerability to MIA across different subgroups in terms of their data d…

Cited by 0SourceScholar
2022

ARCANE: An Efficient Architecture for Exact Machine Unlearning

IJCAI 2022poster

Recently users’ right-to-be-forgotten is stipulated by many laws and regulations. However, only removing the data from the dataset is not enough, as machine learning models would memorize the training data once the data is involved in model training, increasing the risk of exposing users’ privacy. T…

Cited by 117SourcePDFScholar