Funding
- G.1. NSF PDaSP Track 2: Holistic Privacy-Preserving Collaborative Data Sharing for Intelligent Transportation
NSF #2452747, Co-PI (ISU PI)
Funding: $1.2M total ($250K ISU share)
Duration: Oct 2025 – Oct 2028
Lead PI: Yuan Hong (UConn)
Co-PIs: Xuegang Ban (UW), Binghui Wang (IIT)
This project focuses on building a holistic system for privately sharing naturalistic driving data, enabling collaborative research while preserving user privacy. We are very grateful to NSF and FHWA/DOT for their generous support!
Refereed Publications
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Qin Yang, Nicholas Stout, Meisam Mohammady (Corresponding author), Han Wang, Ayesha Samreen, Christopher J Quinn, Yan Yan, Ashish Kundu, Yuan Hong. PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization. Proceedings of the 2025 ACM Conference on Computer and Communications Security (CCS ‘25). *Acceptance rate: TBD.
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Thirasara Ariyarathna, Salil Kanhere, Hye-Young (Helen) Paik, Meisam Mohammady.
FedSIG: Privacy-Preserving Federated Recommendation via Synthetic Interaction Generation.
Proceedings of the 28th International Symposium on Research in Attacks, Intrusions and Defenses (RAID ‘25).
Acceptance rate: TBD. -
M.A.P. Chamikara, Seung Ick Jang, Ian Oppermann, Dongxi Liu, Musotto Roberto, Sushmita Ruj, Arindam Pal, Meisam Mohammady, Seyit Camtepe, Sylvia Young, Chris Dorrian, Nasir David.
Towards Usability of Data with Privacy: A Unified Framework for Privacy-Preserving Data Sharing with High Utility.
Proceedings of the 20th ACM Asia Conference on Computer and Communications Security (ASIACCS ‘25).
Acceptance rate: TBD. -
Shuya Feng, Meisam Mohammady, Hanbin Hong, Shenao Yan, Ashish Kundu, Binghui Wang, Yuan Hong.
Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence.
Proceedings of the Fifteenth ACM Conference on Data and Application Security and Privacy (CODASPY ‘25).
Acceptance rate: TBD. -
Gnanakumar Thedchanamoorthy, Michael Bewong, Meisam Mohammady, Tanveer Zia, Md Zahidul Islam.
UD-LDP: A Technique for Optimally Catalyzing User Driven Local Differential Privacy.
Future Generation Computer Systems (FGCS’25).
Impact Factor: 7.187. -
Mengyuan Zhang, Yosr Jarraya, Makan Pourzandi, Meisam Mohammady, Shangyu Xie, Yuan Hong, Lingyu Wang, Mourad Debbabi.
Utility Optimized Differential Privacy System.
U.S. Patent No. 12321478. -
Shuya Feng*, Meisam Mohammady*, Han Wang, Xiaochen Li, Zhan Qin, Yuan Hong.
DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming.
45th IEEE Symposium on Security and Privacy (S&P ‘24).
Acceptance rate: 202/1389 ≈ 14.5%.
*Equal Contribution (Co-First Authors). -
Gnanakumar Thedchanamoorthy, Michael Bewong, Meisam Mohammady, Tanveer Zia, Md Zahidul Islam.
FUD-LDP: Fully User Driven Local Differential Privacy.
Proceedings of the International Conference on Web Information Systems Engineering (WISE’24).
Acceptance rate: TBD. -
Thirasara Ariyarathna, Meisam Mohammady, Hye-Young (Helen) Paik, Salil S. Kanhere.
VLIA: Navigating Shadows with Proximity for Highly Accurate Visited Location Inference Attack against Federated Recommendation Models.
19th ACM Asia Conference on Computer and Communications Security (ASIACCS’24).
Acceptance rate: 55/284 ≈ 19%. -
Thirasara Ariyarathna, Meisam Mohammady, Hye-Young (Helen) Paik, Salil S. Kanhere.
DeepSneak: User GPS Trajectory Reconstruction from Federated Route Recommendation Models.
ACM Transactions on Intelligent Systems and Technology (ACM TIST’24).
Impact Factor: 10.489. -
Kane Walter, Meisam Mohammady, Surya Nepal, Salil S. Kanhere.
Mitigating Distributed Backdoor Attack in Federated Learning Through Mode Connectivity.
19th ACM Asia Conference on Computer and Communications Security (ASIACCS’24).
Acceptance rate: 55/284 ≈ 19%. -
G. Thedchanamoorthy, M. Bewong, M. Mohammady, T. A. Zia, M. Z. Islam.
Optimization of UD-LDP with Statistical Prior Knowledge.
22nd International Conference on Pervasive Computing and Communications (PerCom 2024).
Acceptance rate: TBD. -
Kane Walter, Meisam Mohammady, Surya Nepal, Salil S. Kanhere.
Optimally Mitigating Backdoor Attacks in Federated Learning.
IEEE Transactions on Dependable and Secure Computing (TDSC’23).
Impact Factor: 7.3. -
Meisam Mohammady, Reza Arablouei.
Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic Analysis.
Symposium on Vehicles Security and Privacy (VehicleSec’23).
Acceptance rate: TBD. -
Meisam Mohammady, Momen Oqaily, Lingyu Wang, Yuan Hong, Habib Louafi, Makan Pourzandi, Mourad Debbabi.
A Multi-view Approach to Preserve Both Privacy and Utility in Network Trace Anonymization.
ACM Transactions on Privacy and Security (TOPS), 2020.
Impact Factor: 3.2. -
Shangyu Xie, Meisam Mohammady, Han Wang, Yuan Hong, Lingyu Wang, Jaideep Vaidya.
Generalizing Prefix-Preserving Data Outsourcing: Ensuring both Privacy and Utility.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.
Impact Factor: 8.881. -
Meisam Mohammady, Shangyu Xie, Yuan Hong, Mengyuan Zhang, Lingyu Wang, Makan Pourzandi, Mourad Debbabi.
R²DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy.
ACM CCS’20.
Acceptance rate: 11%. -
Momen Oqaily, Yosr Jarraya, Meisam Mohammady, Suryadipta Majumdar, Lingyu Wang, Makan Pourzandi, Mourad Debbabi.
SegGuard: Protecting Audit Data Using Segmentation-based Anonymization for Multi-tenant Cloud Auditing.
IEEE TDSC, 2019.
Impact Factor: 6.864. -
Bingyu Liu, Shangyu Xie, Han Wang, Yuan Hong, Xuegang Ban, Meisam Mohammady.
VTDP: Privately Sanitizing Fine-grained Vehicle Trajectory Data with Boosted Utility.
IEEE TDSC, 2019.
Impact Factor: 6.864. -
Suryadipta Majumdar, Azadeh Tabiban, Meisam Mohammady, Alaa Oqaily, Yosr Jarraya, Makan Pourzandi, Lingyu Wang, Mourad Debbabi.
Proactivizer: Transforming Existing Verification Tools into Efficient Solutions for Runtime Security Enforcement.
ESORICS’19.
Acceptance rate: 19.5%. -
Suryadipta Majumdar, Azadeh Tabiban, Meisam Mohammady, Alaa Oqaily, Yosr Jarraya, Makan Pourzandi, Lingyu Wang, Mourad Debbabi.
Multi-Level Proactive Security Auditing for Clouds.
IEEE DSC 2019. -
Meisam Mohammady, Lingyu Wang, Yuan Hong, Habib Louafi, Makan Pourzandi, Mourad Debbabi.
Preserving Both Privacy and Utility in Network Trace Anonymization.
ACM CCS’18.
Acceptance rate: 16.5%. -
Jerome Le Ny, Meisam Mohammady.
Differentially Private MIMO Filtering for Event Streams.
IEEE Transactions on Automatic Control, 2018.
Impact Factor: 5.625. -
Jerome Le Ny, Meisam Mohammady.
Differentially Private MIMO Filtering for Event Streams and Spatio-temporal Monitoring.
CDC’14.
H-index: 118.
Invited Talks
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Preserving Both Privacy and Utility in Network Trace Anonymization
Université du Québec à Montréal (UQAM), Montréal, Canada — November 22, 2019 -
R²DP: A Universal Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
Université du Québec à Montréal (UQAM), Montréal, Canada — November 22, 2019 -
DP-IDS: Differentially Private Intrusion Detection System
Security, Privacy and Forensics (SPF) Seminars, Montréal, Canada — May 10, 2019 -
R²DP: A Universal Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
CSIRO Data61 Reading Seminar, Sydney, Australia — November 22, 2020 -
Novel Approaches to Preserving Utility in Privacy Enhancing Technologies
Discovery Partners Institute (DPI) RD Seminar, Chicago, IL, USA — September 9, 2021
Demonstrations
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Preserving Both Privacy and Utility in Network Trace Anonymization
Ericsson Security Research, Montréal, Canada — May 2018 -
R²DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
Ericsson Security Research, Montréal, Canada — October 2019 -
DPOAD: Differentially Private Outsourcing of Anomaly Detection with Optimal Sensitivity Learning
Ericsson Security Research, Montréal, Canada — October 2020