About

Peihan Miao is an Assistant Professor in the Department of Computer Science at Brown University and is part of Brown's Theory Group. She received her Ph.D. from UC Berkeley in 2019 under the supervision of Sanjam Garg and her BS degree from the ACM Honors Class at Shanghai Jiao Tong University. Her research interests broadly encompass cryptography and security, with a particular focus on secure multi-party computation, both in theoretical and applied contexts. She has received several awards, including the NSF CAREER Award, Meta Research Award, Google Research Scholar Award, and Amazon Research Award. Miao actively contributes to the cryptography community through her research, publications, and involvement in program committees, and she mentors students and researchers in the field.

Research topics

• Computer Science
• Theoretical computer science
• Computer Security
• Computer network
• Mathematics
• Data Mining
• Combinatorics
• Distributed computing
• Discrete mathematics
• Operating system
• Algorithm
• Programming language

Selected publications

• Updatable Private Set Intersection from Symmetric-Key Techniques

  Lecture notes in computer science · 2026-01-01

  book-chapter
  PublisherDOI

• Multi-server Doubly Efficient PIR in the Classical Model and Beyond

  Lecture notes in computer science · 2025-12-04 · 1 citations

  book-chapterOpen access
  PublisherOA PDFDOI

• New Framework for Structure-Aware PSI From Distributed Function Secret Sharing

  Lecture notes in computer science · 2025-12-07

  book-chapterOpen access
  PublisherOA PDFDOI

• Secure and federated quantitative trait loci mapping with privateQTL

  Cell Genomics · 2025-02-01 · 2 citations

  articleOpen access

  Understanding the relationship between genotypes and phenotypes is crucial for advancing personalized medicine. Expression quantitative trait loci (eQTL) mapping plays a significant role by correlating genetic variants to gene expression levels. Despite the progress made by large-scale projects, eQTL mapping still faces challenges in statistical power and privacy concerns. Multi-site studies can increase sample sizes but are hindered by privacy issues. We present privateQTL, a novel framework leveraging secure multi-party computation for secure and federated eQTL mapping. When tested in a real-world scenario with data from different studies, privateQTL outperformed meta-analysis by accurately correcting for covariates and batch effect and retaining higher accuracy and precision for both eGene-eVariant mapping and effect size estimation. In addition, privateQTL is modular and scalable, making it adaptable for other molecular phenotypes and large-scale studies. Our results indicate that privateQTL is a practical solution for privacy-preserving collaborative eQTL mapping.

  PublisherDOI

• Pseudorandom Correlation Generators for Multiparty Beaver Triples over $$\mathbb {F}_2$$

  Lecture notes in computer science · 2025-12-07

  book-chapterOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Finding Balance in Unbalanced PSI: A New Construction from Single-Server PIR

  IACR Communications in Cryptology · 2025-04-08 · 1 citations

  articleOpen access

  Private set intersection (PSI) enables two parties to jointly compute the intersection of their private sets without revealing any extra information to each other. In this work, we focus on the unbalanced setting where one party (a powerful server) holds a significantly larger set than the other party (a resource-limited client). We present a new protocol for this setting that achieves a better balance between low client-side storage and efficient online processing. We first formalize a general framework to transform Private Information Retrieval (PIR) into PSI with techniques used in prior works. Building upon recent advancements in Private Information Retrieval (PIR), specifically the SimplePIR construction (Henzinger et al., USENIX Security'23), combined with our tailored techniques, our construction shows a great improvement in online efficiency. Concretely, when the client holds a single element, our protocol achieves more than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>100</mml:mn> <mml:mi>×</mml:mi> </mml:mrow> </mml:math> faster computation and over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>4</mml:mn> <mml:mi>×</mml:mi> </mml:mrow> </mml:math> lower communication compared to the state-of-the-art unbalanced PSI based on leveled fully homomorphic encryption (Chen et al., CCS'21). The client-side storage is only in the order of tens of megabytes, even for a gigabyte-sized set on the server. Moreover, since the framework is generic, any future improvement in PIR can further improve our construction.

  PublisherOA PDFDOI

• ScanFormer: Referring Expression Comprehension by Iteratively Scanning

  2024-06-16 · 8 citations

  article

  Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely Ref COCO, Ref COCO+, Ref COCO g, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.

  PublisherDOI

• Client-Aided Privacy-Preserving Machine Learning

  Lecture notes in computer science · 2024-01-01 · 1 citations

  book-chapter1st author
  PublisherDOI

• Updatable Private Set Intersection Revisited: Extended Functionalities, Deletion, and Worst-Case Complexity

  Lecture notes in computer science · 2024-12-11 · 8 citations

  book-chapter
  PublisherDOI

• ScanFormer: Referring Expression Comprehension by Iteratively Scanning

  arXiv (Cornell University) · 2024-06-26

  preprintOpen access

  Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.

  PublisherOA PDFDOI

Frequent coauthors

• Sanjam Garg

  14 shared

• Saikrishna Badrinarayanan

  13 shared

• Xi Li

  12 shared

• Tiancheng Xie

  Southeast University

  9 shared

• Wei Su

  Lanzhou University

  8 shared

• Nico Döttling

  7 shared

• Xiaohong Chen

  Hunan Cancer Hospital

  6 shared

• Gaoang Wang

  University of Illinois Urbana-Champaign

  6 shared

Labs

• Peihan Miao LabPI