32 citations · 35 across the 13 of their papers we have counts for
13 papers
What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks
Qin Yang, Lu Malloy, Joshua Lee +4
Large language model (LLM)-powered content moderation systems are a critical defense against harmful online content. However, they operate primarily on tokenized text and often ove…
A Nonlinear Deficiency Identity for the Riemann Zeta Function with Optimal Approximation Rates
Meisam Mohammady
We introduce a deficiency-based representation and approximation framework for values of the Riemann zeta function. The method is based on comparing two nonlinear accumulation mech…
Lap2: Revisiting Laplace DP-SGD for High Dimensions via Majorization Theory
Meisam Mohammady, Qin Yang, Nicholas Stout +4
Differentially Private Stochastic Gradient Descent (DP-SGD) is a cornerstone technique for ensuring privacy in deep learning, widely used in both training from scratch and fine-tun…
PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization
Qin Yang, Nicholas Stout, Meisam Mohammady +6
Differentially Private Stochastic Gradient Descent (DP-SGD) is a standard method for enforcing privacy in deep learning, typically using the Gaussian mechanism to perturb gradient…
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
Shuya Feng, Meisam Mohammady, Hanbin Hong +4
Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tra…
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
Qin Yang, Meisam Mohammad, Han Wang +5
Differentially Private Stochastic Gradient Descent (DP-SGD) and its variants have been proposed to ensure rigorous privacy for fine-tuning large-scale pre-trained language models.…
DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming
Shuya Feng, Meisam Mohammady, Han Wang +3
Streaming data, crucial for applications like crowdsourcing analytics, behavior studies, and real-time monitoring, faces significant privacy risks due to the large and diverse data…
OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy
M. A. P. Chamikara, Seung Ick Jang, Ian Oppermann +9
Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus…
Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic Analysis
Meisam Mohammady, Reza Arablouei
We estimate vehicular traffic states from multimodal data collected by single-loop detectors while preserving the privacy of the individual vehicles contributing to the data. To th…
Randomized Privacy Budget Differential Privacy
Meisam Mohammady
While pursuing better utility by discovering knowledge from the data, individual's privacy may be compromised during an analysis. To that end, differential privacy has been widely…
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning
Meisam Mohammady, Han Wang, Lingyu Wang +6
Outsourcing anomaly detection to third-parties can allow data owners to overcome resource constraints (e.g., in lightweight IoT devices), facilitate collaborative analysis (e.g., u…
RDP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
Meisam Mohammady, Shangyu Xie, Yuan Hong +4
Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications. Since the meaning of data utility in different applications may vastly…
Preserving Both Privacy and Utility in Network Trace Anonymization
Meisam Mohammady, Lingyu Wang, Yuan Hong +3
As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctan…