Copertina

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Tian, Huan, Zhang, Guangsheng, Liu, Bo, Zhu, Tianqing, Ding, Ming, Zhou, Wanlei
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Artificial Intelligence Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers
di: Zhang, Guangsheng, et al.
Pubblicazione: (2022)

Poisoning the Pixels: Revisiting Backdoor Attacks on Semantic Segmentation
di: Zhang, Guangsheng, et al.
Pubblicazione: (2026)

Unique Security and Privacy Threats of Large Language Models: A Comprehensive Survey
di: Wang, Shang, et al.
Pubblicazione: (2024)

Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers
di: Tian, Huan, et al.
Pubblicazione: (2025)

Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
di: Wang, Linlin, et al.
Pubblicazione: (2024)

Privacy Intelligence: A Survey on Image Privacy in Online Social Networks
di: Liu, Chi, et al.
Pubblicazione: (2020)

The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies
di: He, Feng, et al.
Pubblicazione: (2024)

A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks
di: Jebreel, Najeeb, et al.
Pubblicazione: (2026)

Defending Against Neural Network Model Inversion Attacks via Data Poisoning
di: Zhou, Shuai, et al.
Pubblicazione: (2024)

When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
di: Wang, Shang, et al.
Pubblicazione: (2024)

When LLMs Team Up: A Coordinated Attack Framework for Automated Cyber Intrusions
di: Qi, Minfeng, et al.
Pubblicazione: (2026)

Bayes-Nash Generative Privacy Against Membership Inference Attacks
di: Zhang, Tao, et al.
Pubblicazione: (2024)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
di: Xue, Jing, et al.
Pubblicazione: (2025)

Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
di: Xie, Songjie, et al.
Pubblicazione: (2024)

Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks
di: Fu, Haowei, et al.
Pubblicazione: (2025)

Membership Inference Attacks and Privacy in Topic Modeling
di: Manzonelli, Nico, et al.
Pubblicazione: (2024)

Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications
di: Xu, Heng, et al.
Pubblicazione: (2024)

Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
di: Xu, Heng, et al.
Pubblicazione: (2024)

When Reasoning Leaks Membership: Membership Inference Attack on Black-box Large Reasoning Models
di: Hu, Ruihan, et al.
Pubblicazione: (2026)

VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks
di: Tsaprazlis, Efthymios, et al.
Pubblicazione: (2025)

Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
di: Zhang, Chenxiang, et al.
Pubblicazione: (2025)

Membership Reference Attack against Laplace Mechanism of Differential Privacy
di: Huang, Wen
Pubblicazione: (2024)

A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
di: Roy, Falguni, et al.
Pubblicazione: (2024)

Privacy at a Price: Exploring its Dual Impact on AI Fairness
di: Yang, Mengmeng, et al.
Pubblicazione: (2024)

Don't Forget Too Much: Towards Machine Unlearning on Feature Level
di: Xu, Heng, et al.
Pubblicazione: (2024)

The Sample Complexity of Membership Inference and Privacy Auditing
di: Haghifam, Mahdi, et al.
Pubblicazione: (2025)

Unreal Thinking: Chain-of-Thought Hijacking via Two-stage Backdoor
di: Chang, Wenhan, et al.
Pubblicazione: (2026)

Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networks
di: Guan, Faqian, et al.
Pubblicazione: (2024)

FairDP: Certified Fairness with Differential Privacy
di: Tran, Khang, et al.
Pubblicazione: (2023)

Location Privacy Threats and Protections in 6G Vehicular Networks: A Comprehensive Review
di: Ma, Baihe, et al.
Pubblicazione: (2023)

On the Alignment of Group Fairness with Attribute Privacy
di: Aalmoes, Jan, et al.
Pubblicazione: (2022)

PrivCode: When Code Generation Meets Differential Privacy
di: Liu, Zheng, et al.
Pubblicazione: (2025)

Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI
di: Ye, Dayong, et al.
Pubblicazione: (2025)

A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks
di: Liu, Hengzhu, et al.
Pubblicazione: (2024)

Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
di: Liu, Zhenlong, et al.
Pubblicazione: (2024)

Exploring the Integration of Differential Privacy in Cybersecurity Analytics: Balancing Data Utility and Privacy in Threat Intelligence
di: Sedraoui, Brahim Khalil, et al.
Pubblicazione: (2026)

FedFDP: Fairness-Aware Federated Learning with Differential Privacy
di: Ling, Xinpeng, et al.
Pubblicazione: (2024)

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
di: Fu, Jie, et al.
Pubblicazione: (2026)

On Fair Ordering and Differential Privacy
di: Cohen, Shir, et al.
Pubblicazione: (2025)

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning
di: Ahamed, Sayyed Farid, et al.
Pubblicazione: (2024)