Cover Image

SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yekai, Zhang, Rufan, Rong, Wenxin, Mi, Xianghang
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spam
by: Tang, Siyuan, et al.
Published: (2022)

Spam Detection Using BERT
by: Sahmoud, Thaer, et al.
Published: (2022)

Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning
by: Bi, Yu, et al.
Published: (2024)

A Comprehensive Analysis of Adversarial Attacks against Spam Filters
by: Hotoğlu, Esra, et al.
Published: (2025)

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
by: Huang, Wei, et al.
Published: (2025)

Investigating the Effectiveness of Bayesian Spam Filters in Detecting LLM-modified Spam Mails
by: Josten, Malte, et al.
Published: (2024)

SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs
by: Salman, Muhammad, et al.
Published: (2025)

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models
by: SHirvani, Ghazaleh, et al.
Published: (2025)

Leveraging Large Language Models for Cybersecurity: Enhancing SMS Spam Detection with Robust and Context-Aware Text Classification
by: Ahmadi, Mohsen, et al.
Published: (2025)

An Investigation of Large Language Models and Their Vulnerabilities in Spam Detection
by: Tang, Qiyao, et al.
Published: (2025)

StarveSpam: Mitigating Spam with Local Reputation in Permissionless Blockchains
by: Chotkan, Rowdy, et al.
Published: (2025)

The Express Lane to Spam and Centralization: An Empirical Analysis of Arbitrum's Timeboost
by: Messias, Johnnatan, et al.
Published: (2025)

ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection
by: Koide, Takashi, et al.
Published: (2024)

Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
by: Seo, Jungwon, et al.
Published: (2026)

VBSF: A Visual-Based Spam Filtering Technique for Obfuscated Emails
by: Hossary, Ali, et al.
Published: (2025)

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)

Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection
by: Macias-Fassio, Eric, et al.
Published: (2024)

Privacy-Preserving Inference for Quantized BERT Models
by: Lu, Tianpei, et al.
Published: (2025)

Agentic Privacy-Preserving Machine Learning
by: Zhang, Mengyu, et al.
Published: (2025)

Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection
by: Abadi, Aydin, et al.
Published: (2024)

Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
by: Dong, Wenhan, et al.
Published: (2024)

Privacy-Preserving Graph Embedding based on Local Differential Privacy
by: Li, Zening, et al.
Published: (2023)

Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
by: Xie, Chulin, et al.
Published: (2022)

Towards Split Learning-based Privacy-Preserving Record Linkage
by: Zervas, Michail, et al.
Published: (2024)

Enhancing Web Spam Detection through a Blockchain-Enabled Crowdsourcing Mechanism
by: Kader, Noah, et al.
Published: (2024)

Data Privacy Preservation on the Internet of Things
by: Sen, Jaydip, et al.
Published: (2023)

Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)

Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture
by: Fares, Mohamad Haj, et al.
Published: (2024)

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference
by: Luo, Jinglong, et al.
Published: (2024)

Privacy Preserving Adaptive Experiment Design
by: Li, Jiachun, et al.
Published: (2024)

Learning Robust and Privacy-Preserving Representations via Information Theory
by: Zhang, Binghui, et al.
Published: (2024)

Privacy Preserving Reinforcement Learning for Population Processes
by: Yang-Zhao, Samuel, et al.
Published: (2024)

Privacy-Preserving Hierarchical Model-Distributed Inference
by: Dehkordi, Fatemeh Jafarian, et al.
Published: (2024)

Privacy-Preserving Vertical K-Means Clustering
by: Mazzone, Federico, et al.
Published: (2025)

Privacy-Preserving Fair Synthetic Tabular Data
by: Sarmin, Fatima J., et al.
Published: (2025)

Privacy Preservation through Practical Machine Unlearning
by: Dilworth, Robert
Published: (2025)

Privacy-Preserved Neural Graph Databases
by: Hu, Qi, et al.
Published: (2023)

Social-Aware Clustered Federated Learning with Customized Privacy Preservation
by: Wang, Yuntao, et al.
Published: (2022)

Seeing the Unseen: Rethinking Illicit Promotion Detection with In-Context Learning
by: Wu, Sangyi, et al.
Published: (2026)

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization
by: Luo, Xiaoyu, et al.
Published: (2024)