Chenxi Qiu

Dr. Chenxi Qiu is currently a tenure-track assistant professor in The Department of Computer Science and Engineering at University of North Texas (UNT). He directs the Mobile Computing Lab (MCLab) at UNT.

Before joining UNT, he worked as an assistant professor in the Department of Computer Science at Rowan University from 2018 to 2021, and worked as a Postdoctoral Scholar in The College of Information Sciences and Technology at Pennsylvania State University from 2016 to 2018.

Dr. Qiu received his Ph.D. degree in The Holcombe Department of Electrical and Computer Engineering at Clemson University in 2015 and his B.Engr. degree in Telecommunication Engineering from Xidian University in China in 2009.

Dr. Qiu’s research interests span the areas of statistical privacy and optimization for privacy. His research results have been published in highly-competitive conferences and journals (e.g., CCS, USENIX Security, PETS, INFOCOM, AAMAS, IJCAI, EDBT, SIGSPATIAL, IPSN, ICDCS, CIKM, Transactions on Networking (ToN), Transactions on Cloud Computing (TCC), Transactions on Mobile Computing (TMC), Transactions on Parallel and Distributed Systems (TPDS), and Transactions on Computers (TC)). His research has received Distinguished Paper Award at CCS 2025, Best Transactions Paper Award at IEEE T-ITS, and Best Paper Award at CloudCom 2016. His CV can be found here.

news

Jan, 2026	One single-author paper was accepted to USENIX Security 2026.
Oct, 2025	Our work on enhancing the scalability of metric differential privacy optimization has received the ACM CCS Distinguished Paper Award. Congratulations to Ruiyao!
Aug, 2025	One full paper was accepted to ACM CCS 2025. Congratulations to Ruiyao!
Aug, 2025	Two papers (one full and one short) were accepted to ACM SIGSPATIAL 2025. Congratulations to Chenyang, Thanh, Xinpeng, Sourabh, and our collaborators!
Jun, 2025	Congratulations to Sourabh Yadav on successfully defending his Ph.D. dissertation! He will be joining Amazon as an Applied Scientist after graduation.
Jun, 2025	Dr. Qiu was invited to serve as a PC member of ACM SIGSPATIAL 2025 and IEEE INFOCOM 2026.
Mar, 2025	Dr. Qiu was invited to serve as a PC member of IEEE BigData 2025, ACM CIKM 2025, PETS 2026, and the Editorial Board of PoPETs.
Feb, 2025	Dr. Qiu has joined the IEEE TDSC Editorial Board as an Associate Editor.
Feb, 2025	Dr. Qiu was invited to serve as a PC member for IEEE ICFEC 2025 and EAI SecureComm 2025.
Nov, 2024	One full papers was accepted to PETS 2025. Congratulations to Ruiyao, Xinpeng, and our collaborators!

selected publications

[C] USENIX Sec 2026

[A+] Interpolation-Based Optimization for Enforcing lp-Norm Metric Differential Privacy in Continuous and Fine-Grained Domains

Chenxi Qiu

In Proceedings of The 35th USENIX Security Symposium, 2026
[C] CCS 2025

[A+] PAnDA: Rethinking Metric Differential Privacy Optimization at Scale with Anchor-Based Approximation

*Ruiyao Liu, and Chenxi Qiu

In Proceedings of The 32nd ACM Conference on Computer and Communications Security (CCS), 2025

Distinguished Paper Award
[C] PETS 2025

[A] Time-Efficient Locally Relevant Geo-Location Privacy Protection

Chenxi Qiu, *Ruiyao Liu, Primal Pappachan, and 2 more authors

In Proceedings of the 25th Privacy Enhancing Technologies Symposium (PETS), 2025
[J] TMC 2020

[A+] Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing via Geo-Indistinguishability

Chenxi Qiu, Anna Squicciarini, *Ce Pang, and 2 more authors

IEEE Transactions on Mobile Computing [Impact factor = 6.075], 2020

Abs HTML PDF

Nowadays, vehicles have been increasingly adopted in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location to servers to facilitate the service utilities. Traditional location privacy protection mechanisms cannot be applied to vehicle-based SC since they assume workers mobility on a 2-dimensional plane without considering the network-constrained mobility features of vehicles. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) over road networks. Our objective is to design a location obfuscation strategy to minimize the quality loss due to obfuscation with geo-indistinguishability satisfied. Considering the computational complexity of VLP, by resorting to discretization, we first approximate VLP to a linear programming problem that can be solved by well-developed approaches. To further improve the time efficiency, we conduct constraint reduction for VLP by exploiting key features of geo-indistinguishability in road networks and problem decomposition based on VLPs constraint structure. Finally, we carry out both trace-driven simulation and real-world experiments, where our experimental results demonstrate the superiority of our approach over a known state-of-the-art location obfuscation strategy in terms of both QoS and privacy.