Image de couverture de livre

Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chen, Gaoyi, Li, Minghao, Shi, Weishi, Huang, Yan, Wei, Yusheng, Yadav, Sourabh, Qiu, Chenxi
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Protecting Vehicle Location Privacy with Contextually-Driven Synthetic Location Generation
par: Yadav, Sourabh, et autres
Publié: (2024)

A Decade of Metric Differential Privacy: Advancements and Applications
par: Xie, Xinpeng, et autres
Publié: (2025)

PAnDA: Rethinking Metric Differential Privacy Optimization at Scale with Anchor-Based Approximation
par: Liu, Ruiyao, et autres
Publié: (2025)

Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition
par: Qiu, Chenxi
Publié: (2024)

Interpolation-Based Optimization for Enforcing lp-Norm Metric Differential Privacy in Continuous and Fine-Grained Domains
par: Qiu, Chenxi
Publié: (2026)

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
par: Meng, Jiayang, et autres
Publié: (2025)

Attacker Control and Bug Prioritization
par: Lacombe, Guilhem, et autres
Publié: (2025)

The First VoicePrivacy Attacker Challenge
par: Tomashenko, Natalia, et autres
Publié: (2025)

Observable Channels, Not Just Storage: Evaluating Privacy Leakage in LLM Agent Pipelines
par: Huang, Tao, et autres
Publié: (2026)

Time-Efficient Locally Relevant Geo-Location Privacy Protection
par: Qiu, Chenxi, et autres
Publié: (2024)

Evaluating Privacy Leakage in Split Learning
par: Qiu, Xinchi, et autres
Publié: (2023)

A Descriptive Model for Modelling Attacker Decision-Making in Cyber-Deception
par: Turner, B. R., et autres
Publié: (2025)

Computing Maximal Per-Record Leakage and Leakage-Distortion Functions for Privacy Mechanisms under Entropy-Constrained Adversaries
par: Wu, Genqiang, et autres
Publié: (2026)

A Game Between the Defender and the Attacker for Trigger-based Black-box Model Watermarking
par: Huang, Chaoyue, et autres
Publié: (2025)

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
par: Shi, Haonan, et autres
Publié: (2025)

CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning
par: Wang, Weiqi, et autres
Publié: (2025)

SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling
par: Ganev, Georgi, et autres
Publié: (2025)

Enhanced Privacy Leakage from Noise-Perturbed Gradients via Gradient-Guided Conditional Diffusion Models
par: Meng, Jiayang, et autres
Publié: (2025)

The First VoicePrivacy Attacker Challenge Evaluation Plan
par: Tomashenko, Natalia, et autres
Publié: (2024)

PrivTru: A Privacy-by-Design Data Trustee Minimizing Information Leakage
par: Gehring, Lukas, et autres
Publié: (2025)

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
par: Fu, Jie, et autres
Publié: (2026)

Forecasting Attacker Actions using Alert-driven Attack Graphs
par: Băbălău, Ion, et autres
Publié: (2024)

Attackers reveal their arsenal: An investigation of adversarial techniques in CTI reports
par: Rahman, Md Rayhanur, et autres
Publié: (2024)

The Hidden Cost of Correlation: Rethinking Privacy Leakage in Local Differential Privacy
par: Jayawardana, Sandaru, et autres
Publié: (2025)

PSY: Posterior Sampling Based Privacy Enhancer in Large Language Models
par: Sun, Yulian, et autres
Publié: (2024)

Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis
par: Deng, Ruijun, et autres
Publié: (2025)

ToDA: Target-oriented Diffusion Attacker against Recommendation System
par: Liu, Xiaohao, et autres
Publié: (2024)

Analysis of Privacy Leakage in Federated Large Language Models
par: Vu, Minh N., et autres
Publié: (2024)

A Framework for Managing Multifaceted Privacy Leakage While Optimizing Utility in Continuous LBS Interactions
par: Bkakria, Anis, et autres
Publié: (2024)

PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning
par: Hou, Sizai, et autres
Publié: (2024)

Uncover the Premeditated Attacks: Detecting Exploitable Reentrancy Vulnerabilities by Identifying Attacker Contracts
par: Yang, Shuo, et autres
Publié: (2024)

Selfish Mining in Multi-Attacker Scenarios: An Empirical Evaluation of Nakamoto, Fruitchain, and Strongchain
par: Perešíni, Martin, et autres
Publié: (2026)

Powering the Future of IoT: Federated Learning for Optimized Power Consumption and Enhanced Privacy
par: Shirvani, Ghazaleh, et autres
Publié: (2024)

PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy
par: Wang, Leixia, et autres
Publié: (2024)

Defending Against Intelligent Attackers at Large Scales
par: Lohn, Andrew J.
Publié: (2025)

Quantum Disruption: An SOK of How Post-Quantum Attackers Reshape Blockchain Security and Performance
par: Mallick, Tushin, et autres
Publié: (2025)

A Review of Privacy Metrics for Privacy-Preserving Synthetic Data Generation
par: Trudslev, Frederik Marinus, et autres
Publié: (2025)

EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems
par: Chen, Kongyang, et autres
Publié: (2024)

Membership Information Leakage in Federated Contrastive Learning
par: Chen, Kongyang, et autres
Publié: (2024)

RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakage
par: Zhong, Peter Yong, et autres
Publié: (2025)