Nan Cheng

Nan Cheng

Phd Student

University of St. Gallen

About Me

I am a researcher in the field of multi-party secure computation, with a focus on reducing communication complexity of MPC protocols for either general or specific compuation problems. My research goal is to promote the adaption of MPC-based PETs (privacy-enhancing technologies) into real-world industries.

Interests

Multi-party Secure Computation
Function Secret Sharing
Pseudorandom Correlations

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 more 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 😃

Featured Publications

Efficient Two-Party Secure Aggregation via Incremental Distributed Point Function

Incremental Function Secret Sharing

Efficient Two-Party Secure Aggregation via Incremental Distributed Point Function

Computing the maximum from a list of secret inputs is a widely-used functionality that is employed either indirectly as a building block in secure computation frameworks, such as ABY (NDSS'15) or directly used in multiple applications that solve optimisation problems, such as secure machine learning or secure aggregation statistics. {\em Incremental distributed point function} (I-DPF) is a powerful primitive (IEEE S\&P'21) that significantly reduces the client-to-server communication and are employed to efficiently and securely compute aggregation statistics. Our protocols have a communication complexity that is independent of the number of secret inputs and linear to the length of the secret input domain. Our experimental results show enhanced efficiency over state-of-the-art solutions, particularly notable when handling large-scale inputs. For instance, in scenarios involving an input set of five million elements with an input domain size of 31 bits, our protocol \(\Pi_{\mathsf{Max}}\) achieves an $18\%$ reduction in online execution time and a $67\%$ decrease in communication volume compared to the most efficient existing solution.

Jul 2, 2024

Nomadic: Normalising Maliciously-Secure Distance with Cosine Similarity for Two-Party Biometric Authentication

Function Secret Sharing

Nomadic: Normalising Maliciously-Secure Distance with Cosine Similarity for Two-Party Biometric Authentication

Computing the distance between two non-normalized vectors $\mathbfit{x}$ and $\mathbfit{y}$, represented by $\Delta(\mathbfit{x},\mathbfit{y})$ and comparing it to a predefined public threshold $\tau$ is an essential functionality used in privacy-sensitive applications such as biometric authentication, identification, machine learning algorithms ({\em e.g.,} linear regression, k-nearest neighbors, etc.), and typo-tolerant password-based authentication.

Jul 1, 2024

A post-quantum Distributed OPRF from the Legendre PRF

Oblivious Pseudorandom Functions

A post-quantum Distributed OPRF from the Legendre PRF

We present a post-quantum OPRF in a distributed server setting, which can be computed in a single round of communication between a client and the servers. The only server-to-server communication occurs during a precomputation phase.

Apr 8, 2024

Constant-Round Private Decision Tree Evaluation for Secret Shared Data

Privacy-Preserving Machine Learning

Constant-Round Private Decision Tree Evaluation for Secret Shared Data

Our proposed protocol only requires five rounds among the employed three servers executing secret sharing schemes, which is comparable to previously proposed protocols that are based on garbled circuits and homomorphic encryption. To further demonstrate the efficiency of our protocol, we evaluated it using real-world classification datasets. The evaluation results indicate that our protocol provides better concrete performance in the WAN setting that has a large network delay.

Jan 1, 2024

Recent Publications

Nan Cheng, Aikaterini Mitrokotsa, Feng Zhang, Frank Hartmann (2024). Efficient Two-Party Secure Aggregation via Incremental Distributed Point Function. IEEE Euro S&P 2024.

Nan Cheng, Melek Önen, Aikaterini Mitrokotsa, Oubaïda Chouchane, Massimiliano Todisco, Alberto Ibarrondo (2024). Nomadic: Normalising Maliciously-Secure Distance with Cosine Similarity for Two-Party Biometric Authentication. Asia CCS 2024.

Novak Kaluderovic, Nan Cheng, Aikaterina Mitrokotsa (2024). A post-quantum Distributed OPRF from the Legendre PRF. eprint 2024.

Nan Cheng, Naman Gupta, Aikaterini Mitrokotsa, Hiraku Morita, Kazunari Tozawa (2024). Constant-Round Private Decision Tree Evaluation for Secret Shared Data. PETS 2024.

Nan Cheng, Feng Zhang, Aikaterini Mitrokotsa (2023). Efficient Three-party Boolean-to-Arithmetic Share Conversion. PST 2023.

Recent News

2024 TPMPC Workshop

Jun 3, 2024

I attended the 2024 TPMPC Workshop held at TU Darmstadt.

Jun 3, 2024