Visual Privacy Auditing with Diffusion Models
Fuente:
arXiv
Guardado en:
| Autores principales: | Schwethelm, Kristian, Kaiser, Johannes, Knolle, Moritz, Lockfisch, Sarah, Rueckert, Daniel, Ziller, Alexander |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
por: Riess, Anneliese, et al.
Publicado: (2024)
por: Riess, Anneliese, et al.
Publicado: (2024)
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
por: Schwethelm, Kristian, et al.
Publicado: (2024)
por: Schwethelm, Kristian, et al.
Publicado: (2024)
Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy
por: Kaiser, Johannes, et al.
Publicado: (2026)
por: Kaiser, Johannes, et al.
Publicado: (2026)
Complex-valued Federated Learning with Differential Privacy and MRI Applications
por: Riess, Anneliese, et al.
Publicado: (2021)
por: Riess, Anneliese, et al.
Publicado: (2021)
On Arbitrary Predictions from Equally Valid Models
por: Lockfisch, Sarah, et al.
Publicado: (2025)
por: Lockfisch, Sarah, et al.
Publicado: (2025)
Observational Auditing of Label Privacy
por: Kalemaj, Iden, et al.
Publicado: (2025)
por: Kalemaj, Iden, et al.
Publicado: (2025)
Sensitivity, Specificity, and Consistency: A Tripartite Evaluation of Privacy Filters for Synthetic Data Generation
por: Koeken, Adil, et al.
Publicado: (2025)
por: Koeken, Adil, et al.
Publicado: (2025)
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
por: Kazmi, Mishaal, et al.
Publicado: (2024)
por: Kazmi, Mishaal, et al.
Publicado: (2024)
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
por: Cebere, Tudor, et al.
Publicado: (2024)
por: Cebere, Tudor, et al.
Publicado: (2024)
Auditing $f$-Differential Privacy in One Run
por: Mahloujifar, Saeed, et al.
Publicado: (2024)
por: Mahloujifar, Saeed, et al.
Publicado: (2024)
Optimizing Canaries for Privacy Auditing with Metagradient Descent
por: Boglioni, Matteo, et al.
Publicado: (2025)
por: Boglioni, Matteo, et al.
Publicado: (2025)
The Sample Complexity of Membership Inference and Privacy Auditing
por: Haghifam, Mahdi, et al.
Publicado: (2025)
por: Haghifam, Mahdi, et al.
Publicado: (2025)
Sequentially Auditing Differential Privacy
por: González, Tomás, et al.
Publicado: (2025)
por: González, Tomás, et al.
Publicado: (2025)
Privacy Auditing of Large Language Models
por: Panda, Ashwinee, et al.
Publicado: (2025)
por: Panda, Ashwinee, et al.
Publicado: (2025)
Auditing Differential Privacy Guarantees Using Density Estimation
por: Koskela, Antti, et al.
Publicado: (2024)
por: Koskela, Antti, et al.
Publicado: (2024)
Auditing Privacy Mechanisms via Label Inference Attacks
por: Busa-Fekete, Róbert István, et al.
Publicado: (2024)
por: Busa-Fekete, Róbert István, et al.
Publicado: (2024)
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
por: Yoon, Sangyeon, et al.
Publicado: (2024)
por: Yoon, Sangyeon, et al.
Publicado: (2024)
Instance-Level Data-Use Auditing of Visual ML Models
por: Huang, Zonghao, et al.
Publicado: (2025)
por: Huang, Zonghao, et al.
Publicado: (2025)
How Well Can Differential Privacy Be Audited in One Run?
por: Keinan, Amit, et al.
Publicado: (2025)
por: Keinan, Amit, et al.
Publicado: (2025)
Auditing Differential Privacy in the Black-Box Setting
por: Shi, Kaining, et al.
Publicado: (2025)
por: Shi, Kaining, et al.
Publicado: (2025)
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
por: Liu, Terrance, et al.
Publicado: (2025)
por: Liu, Terrance, et al.
Publicado: (2025)
Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
por: Ward, Joshua, et al.
Publicado: (2025)
por: Ward, Joshua, et al.
Publicado: (2025)
PrivDiffuser: Privacy-Guided Diffusion Model for Data Obfuscation in Sensor Networks
por: Yang, Xin, et al.
Publicado: (2024)
por: Yang, Xin, et al.
Publicado: (2024)
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
por: Sahani, Mukesh, et al.
Publicado: (2025)
por: Sahani, Mukesh, et al.
Publicado: (2025)
Tight Auditing of Differential Privacy in MST and AIM
por: Ganev, Georgi, et al.
Publicado: (2026)
por: Ganev, Georgi, et al.
Publicado: (2026)
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
por: Kaissis, Georgios, et al.
Publicado: (2024)
por: Kaissis, Georgios, et al.
Publicado: (2024)
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk
por: Li, Zhangheng, et al.
Publicado: (2024)
por: Li, Zhangheng, et al.
Publicado: (2024)
Auditing Privacy in Multi-Tenant RAG under Account Collusion
por: Burnat, Florian A. D.
Publicado: (2026)
por: Burnat, Florian A. D.
Publicado: (2026)
Laplace Sample Information: Data Informativeness Through a Bayesian Lens
por: Kaiser, Johannes, et al.
Publicado: (2025)
por: Kaiser, Johannes, et al.
Publicado: (2025)
Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework
por: Cartmell, John, et al.
Publicado: (2026)
por: Cartmell, John, et al.
Publicado: (2026)
Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
por: Luo, Jiayi, et al.
Publicado: (2025)
por: Luo, Jiayi, et al.
Publicado: (2025)
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
por: Usynin, Dmitrii, et al.
Publicado: (2023)
por: Usynin, Dmitrii, et al.
Publicado: (2023)
The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
por: Meeus, Matthieu, et al.
Publicado: (2025)
por: Meeus, Matthieu, et al.
Publicado: (2025)
A General Framework for Data-Use Auditing of ML Models
por: Huang, Zonghao, et al.
Publicado: (2024)
por: Huang, Zonghao, et al.
Publicado: (2024)
Enhancing Privacy in ControlNet and Stable Diffusion via Split Learning
por: Yao, Dixi
Publicado: (2024)
por: Yao, Dixi
Publicado: (2024)
BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding
por: Benoit, Tristan, et al.
Publicado: (2024)
por: Benoit, Tristan, et al.
Publicado: (2024)
Breaking Bad: Interpretability-Based Safety Audits of State-of-the-Art LLMs
por: Agarwal, Krishiv, et al.
Publicado: (2026)
por: Agarwal, Krishiv, et al.
Publicado: (2026)
Privacy-preserving gradient-based fair federated learning
por: Adamek, Janis, et al.
Publicado: (2024)
por: Adamek, Janis, et al.
Publicado: (2024)
Quantifying Memorization and Privacy Risks in Genomic Language Models
por: Nemecek, Alexander, et al.
Publicado: (2026)
por: Nemecek, Alexander, et al.
Publicado: (2026)
To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2024)
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2024)
Ejemplares similares
-
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
por: Riess, Anneliese, et al.
Publicado: (2024) -
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
por: Schwethelm, Kristian, et al.
Publicado: (2024) -
Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy
por: Kaiser, Johannes, et al.
Publicado: (2026) -
Complex-valued Federated Learning with Differential Privacy and MRI Applications
por: Riess, Anneliese, et al.
Publicado: (2021) -
On Arbitrary Predictions from Equally Valid Models
por: Lockfisch, Sarah, et al.
Publicado: (2025)