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I am a Ph.D. at UC Riverside, where I am fortunately advised by Prof. Nael Abu-Ghazaleh. Prior to that, I received my M.Sc. from UC Irvine, and my B.S. from Sichuan University. I worked with Prof. Mohammad Abdullah Al Faruque and Prof. Zhou Li in the past. My interest lies in system security and computer architecture.
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– May 2022: Got acceptance to attend [gem5 Bootcamp'22]. – July 2021: Received student travel grant from [Security’21]. – July 2021: One paper accepted by [IEEE TIFS]. – May 2021: Received student travel grant from [S&P’21]. – February 2021: One poster accepted by [FPGA'21]. |
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| Ph.D. in Electrical Engineering | 2021-2025 |
| University of California, Riverside | Riverside, California | |
| M.Sc. in Computer Engineering | 2018-2020 |
| University of California, Irvine | Irvine, California | |
| B.S. in Electrical Engineering and Automation | 2014-2018 |
| Sichuan University | Chengdu, Sichuan |
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We demonstrate that today's AR systems are vulnerable to side-channel attacks that can be launched from software; a malicious application without any special permissions can infer private information about other concurrent applications, or even the surrounding world. We first present a taxonomy of potential attacks targeting user interaction models, concurrent applications, and real-world environments. We developed four end-to-end attacks from these three categories. We believe the threats presented by our attacks are pressing, and a new multi-application AR platform should be developed against this threat. |
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We explore whether the structural secret (layers and hyper-parameters) of a victim DNN model can be inferred by a remote attacker who shares the same FPGA board. We show by implementing on-chip RO-based power monitors, the power consumption of the victim DNN can be sampled at high resolution, which guarantees all layers and hyper-parameters can be reconstructed at high accuracy. |
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We exploit the GPU side-channel based on context-switching penalties. This side-channel allows us to extract the fine-grained structural secret of a DNN model, including its layer composition and hyper-parameters. Leveraging this side-channel, we developed an attack prototype named MosConS, which applies LSTM-based inference models to identify the structural secret. |
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| Student Travel Grant | 30th USENIX Security Symposium | 2021 |
| Student Travel Grant | 42nd IEEE Symposium on Security and Privacy | 2021 |
| Dean's Distinguished Fellowship Award | UC Riverside | 2021 |
| Sichuan University Scholarship | Sichuan University | 2014-2018 |
| Outstanding Students Leader | Sichuan University | 2016 |
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While at UC Irvine, I was a teaching assistant for the following courses:
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Reviewer: ICPS' 20, CYBER' 21, CYBER' 22 Sub-reviewer: DATE'19, CODES+ISSS'20, DAC'20, NDSS'20, DSN'20, RTSS'21, MICRO'22 Artifact Evaluation: MICRO'22 |
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In my free time, I love to ride road bikes. In the summer of 2018, I cycled around Taiwan Province with my friends. I was a member of University of California Irvine Cycling Club and was the leader of Sichuan University Cycling Club. Also, I like to capture the beautiful moments of our life stories with my wife, Jinrun. Subscribe our channel and watch our Vlog! (๑•̀ㅂ•́)و✧ |
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