# Xiling Li

> master of Computer Science & Engineering, University of Washington, 2020

**Wikidata**: [Q113667805](https://www.wikidata.org/wiki/Q113667805)  
**Source**: https://4ort.xyz/entity/xiling-li

## Summary
Xiling Li is a computer scientist and graduate of the University of Washington, where she earned a master’s degree in Computer Science & Engineering in 2020. She is known for her research on privacy-preserving data processing techniques, particularly her thesis on secure feature selection using multiparty computation.

## Biography
- Born: [Date and place unknown]  
- Nationality: [Unknown]  
- Education: Master’s degree in Computer Science & Engineering, University of Washington (2020)  
- Known for: Research in privacy-preserving feature selection and secure multiparty computation  
- Employer(s): [Not specified]  
- Field(s): Computer science, computer engineering  

## Contributions  
Xiling Li’s work focuses on developing secure methods for data analysis. Her master’s thesis, *Privacy-preserving Filter-based Feature Selection with Secure Multiparty Computation* (2020), addresses challenges in preserving data privacy during feature selection processes. This research contributes to techniques for secure multiparty computation, enabling collaborative data analysis without compromising sensitive information. While specific applications or adoption metrics are not detailed in the source material, her work aligns with growing demand for privacy-centric technologies in fields like healthcare and finance.  

## FAQs  
### Q: Where did Xiling Li complete her graduate studies?  
A: She earned her master’s degree in Computer Science & Engineering at the University of Washington in 2020.  

### Q: What is Xiling Li’s notable research contribution?  
A: Her thesis, *Privacy-preserving Filter-based Feature Selection with Secure Multiparty Computation*, explores secure data processing methods.  

### Q: Who supervised Xiling Li’s academic work?  
A: She was a student of Martine De Cock.  

## Why They Matter  
Xiling Li’s research advances privacy-preserving data analysis, a critical area as organizations increasingly rely on shared datasets while facing regulatory and ethical privacy constraints. Her work on secure multiparty computation provides foundational insights for developing tools that balance data utility and confidentiality. As data breaches and privacy concerns grow, contributions like Li’s help enable secure collaboration in fields such as medical research and financial systems, where sensitive information is often involved.  

## Notable For  
- Master’s thesis: *Privacy-preserving Filter-based Feature Selection with Secure Multiparty Computation* (2020)  
- Graduate of the University of Washington’s Computer Science & Engineering program  
- Student of Martine De Cock  

## Body  
### Education and Academic Work  
Xiling Li completed her master’s degree in Computer Science & Engineering at the University of Washington in 2020. Her studies focused on computer science and computer engineering, culminating in a thesis under the supervision of Martine De Cock.  

### Thesis  
Li’s thesis, *Privacy-preserving Filter-based Feature Selection with Secure Multiparty Computation*, investigates methods for selecting data features while maintaining privacy through secure multiparty computation. This work is relevant to scenarios where multiple parties collaborate on data analysis without exposing sensitive information.  

### Professional Background  
No specific employment or post-graduation roles are detailed in the source material. Her academic contributions are centered on her graduate research at the University of Washington.

## References

1. WorldCat