Xuanle Ren (任轩乐)

Xuanle Ren Assistant Professor / 高级工程师 Post-Moore Microelectronics Integrated Circuit Center (PMICC) School of Information Science and Technology (SIST) ShanghaiTech University Email: renxl@shanghaitech.edu.cn Office: Room 306, SIST Building 3, 393 Huaxia Middle Road, Pudong New Area, Shanghai, China 上海市浦东新区华夏中路393号上海科技大学信息学院3号楼306

• I am a tenure-track Assistant Professor in ShanghaiTech University, and lead the SPARC (Security, Privacy, and ARChitecture) Lab. My research is focused on hardware acceleration, algorithm design, and application for privacy-preserving computing, including homomorphic encryption, trusted execution environment, and zero-knowledge proof. I am also interested in developing secure and trusted hardware systems, and exploring novel EDA algorithms. I received the Ph.D. degree from Carnegie Mellon University in 2018 (Advisors: Prof. Shawn Blanton and Prof. Vítor Grade Tavares), and the B.S. degree from Peking University in 2012 (Advisor: Prof. Zhongjian Chen).
  

• 实验室正在招收2025年秋季入学的保研、统考硕士研究生 (电子科学与技术/0809学术学位) 和科研助理，具体说明请点击这里。请有意向的同学通过邮箱发送以下材料：(1) 简历和成绩单；(2) 科研和竞赛经历与成果，并附上相关证明材料 (如有); (3) 研究计划 (科研助理需提交，研究生可选)。
  
  

Research

In the era of big data, data has become the driving force behind an increasing number of applications. However, acquiring data is not always straightforward, and sharing it is often prohibited for certain organizations, such as healthcare systems, banks, and governments. Privacy-preserving computation seeks to address this issue by enabling the sharing of only encrypted data, thus preventing the exposure of sensitive information. To achieve this goal, the community has developed several protocols and algorithms, including secure multi-party computation, homomorphic encryption, and federated learning. These approaches allow multiple parties to collaboratively train a shared predictive model with minimal data exchange. However, these algorithms often incur significant computational overhead. Designing efficient and practical algorithms, software, and hardware remains a critical research challenge.

Therefore, my research focuses on the following topics:

• Homomorphic Encryption (HE) algorithm and architecture design, especially heterogeneous system design
• Algorithm and protocol design for privacy preservation
• Privacy-preserving systems based on trusted execution environments (TEEs)
• AI-driven EDA algorithm design

Experience

• Research Scientist, Hash Innovation, Bitmain (比特大陆), 2022.10-2024.11

• Research Scientist, Computing Technology Lab, DAMO Academy, Alibaba Group (阿里巴巴达摩院), 2018.11-2022.10

• Research Assistant, Advanced Chip Test Lab (ACTL), Carnegie Mellon University, 2012.9-2018.9

Education

• Ph.D., Electrical and Computer Engineering, Carnegie Mellon University, 2018

• B.S., Microelectronics, Peking University, 2012

• B.S., Economics (Double Degree), Peking University, 2012

Publications

• SAFE: A Scalable Homomorphic Encryption Accelerator for Vertical Federated Learning, by Z. Chen, Z. Gu, Y. Lu, X. Ren, R. Zhong, W. Lu, J. Zhang, Y. Zhang, H. Wu, X. Zheng, H. Liu, T. Chu, C. Hong, C. Wei, D. Niu, Y. Xie [pdf]
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024. CCF-A

• CHAM: A Customized Homomorphic Encryption Accelerator For Fast Matrix-Vector Product, by X. Ren, Z. Chen, Z. Gu, Y. Lu, R. Zhong, W. Lu, J. Zhang, Y. Zhang, H. Wu, X. Zheng, H. Liu, T. Chu, C. Hong, C. Wei, D. Niu, Y. Xie [pdf][中文]
  Design Automation Conference (DAC), 2023. CCF-A

• HEDA: Multi-Attribute Unbounded Aggregation Over Homomorphically Encrypted Database, by X. Ren, L. Su, S. Bian, S. Wang, F. Li, C. Li, F. Zhang, Y. Xie [pdf]
  International Conference on Very Large Data Bases (VLDB), 2022. CCF-A

• Design And Evaluation Of Fluctuating Power Logic To Mitigate Power Analysis At The Cell Level, by F. Zhang, B. Yang, B. Yang, Y. Zhang, X. Ren, S. Bhasin, K. Ren [pdf]
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021. CCF-A

• Customizing Trusted AI Accelerators For Efficient Privacy-Preserving Machine Learning, by P. Xie, X. Ren, G. Sun [pdf][中文]
  arXiv:2011.06376, 2020.

• An Enclave-Based TEE For SE-In-SoC In RISC-V Industry, by X. Ren, X. Cui [pdf]
  Embedded World Exhibition, 2020.

• IC Protection Against JTAG-Based Attacks, by X. Ren, F. Torres, S. Blanton, V. Tavares [pdf]
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2019. CCF-A

• Detection Of IJTAG Attacks Using LDPC-Based Feature Reduction And Machine Learning, by X. Ren, S. Blanton, V. Tavares [pdf]
  IEEE European Test Symposium (ETS), 2018.

• A Learning-Based Approach To Secure JTAG Against Unseen Scan-Based Attacks, by X. Ren, S. Blanton, V. Tavares [pdf]
  IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2016.

• Detection Of Illegitimate Access To JTAG Via Statistical Learning In Chip, by X. Ren, V. Tavares, S. Blanton [pdf]
  Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015. CCF-B

• Improving Accuracy Of On-Chip Diagnosis Via Incremental Learning, by X. Ren, M. Martin, S. Blanton [pdf]
  IEEE VLSI Test Symposium (VTS), 2015.

Talks

• Hardware Acceleration for Fully Homomorphic Encryption (Invited Talk)
  Design Automation Conference (DAC), 2022

Teaching

• Spring 2025: EE2110, Testable Design for Digital Integrated Circuits

Awards

• 上海市工程系列集成电路专业, 高级工程师职称, 2023

• "上海产业菁英"高层次人才, 上海市经济和信息化委员会, 2021

• 优秀自费留学生奖学金, 国家留学基金委, 2018

• Carnegie Mellon Porgual PhD Fellowship, 2012-2017