Nan Cheng
Nan Cheng

Phd Student

About Me

I specialize in multi-party secure computation, with a focus on reducing communication complexity of MPC protocols for either general or specific compuation problems. I hope my work will help accelerate the adaption of privacy-enhancing technologies (PETs) in real-world industries.

Outside of my research, I enjoy hiking and reading a wide range of books, particularly in history and philosophy.

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Interests
  • Multi-party Secure Computation
  • Zero knowledge proof systems
  • Post quantum cryptography
Education
  • PhD Applied Cryptography

    University of St. Gallen

  • MEng Applied Cryptography

    Fudan University

  • BSc Computer Science

    Jiangxi Agricultural University

🔍 My Research

Function secret sharing is a primitive that effectively computes some of the non-liear functionalites in one online evaluation round, at the cost of larger computation overhead at the meantime. My research focuses on multi-party secure computation, specifically on enhancing concrete efficiency through the design of more efficient cryptographic primitives or the design of specialized protocols. These protocols aim to improve efficiency in various secure computation challenges, including Private Set Intersection (PSI), secure machine learning inference, and secure aggregation, etc. My goal is to integrate efficient MPC technologies into practical applications.

Recently, in addition to designing concrete MPC protocols, I have been focusing on the application of Pseudorandom Correlated Generators (PCGs).

If you share similar intersts, please feel free to contact me for collaborations 😃

📚 Recent Publications
(2024). Efficient Two-Party Secure Aggregation via Incremental Distributed Point Function. IEEE Euro S&P 2024.
(2024). Nomadic: Normalising Maliciously-Secure Distance with Cosine Similarity for Two-Party Biometric Authentication. Asia CCS 2024.
(2024). A post-quantum Distributed OPRF from the Legendre PRF. eprint 2024.
(2024). Constant-Round Private Decision Tree Evaluation for Secret Shared Data. PETS 2024.
(2023). Efficient Three-party Boolean-to-Arithmetic Share Conversion. PST 2023.
Recent News

2024 TPMPC Workshop

I attended the 2024 TPMPC Workshop held at TU Darmstadt.