Georgi Ganev
Orcid: 0009-0004-4287-7473
  According to our database1,
  Georgi Ganev
  authored at least 20 papers
  between 2021 and 2025.
  
  
Collaborative distances:
Collaborative distances:
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Bibliography
  2025
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation.
    
  
    CoRR, June, 2025
    
  
    CoRR, April, 2025
    
  
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data.
    
  
    CoRR, April, 2025
    
  
    Trans. Mach. Learn. Res., 2025
    
  
The Inadequacy of Similarity-Based Privacy Metrics: Privacy Attacks Against "Truly Anonymous" Synthetic Datasets.
    
  
    Proceedings of the IEEE Symposium on Security and Privacy, 2025
    
  
    Proceedings of the Computer Security - ESORICS 2025, 2025
    
  
  2024
    CoRR, 2024
    
  
    CoRR, 2024
    
  
"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation.
    
  
    Proceedings of the 33rd USENIX Security Symposium, 2024
    
  
Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility.
    
  
    Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024
    
  
  2023
On the Inadequacy of Similarity-based Privacy Metrics: Reconstruction Attacks against "Truly Anonymous Synthetic Data".
    
  
    CoRR, 2023
    
  
    CoRR, 2023
    
  
Understanding how Differentially Private Generative Models Spend their Privacy Budget.
    
  
    CoRR, 2023
    
  
    Proceedings of the Eleventh International Conference on Learning Representations, 2023
    
  
  2022
dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation.
    
  
    CoRR, 2022
    
  
    Proceedings of the 29th Annual Network and Distributed System Security Symposium, 2022
    
  
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data.
    
  
    Proceedings of the International Conference on Machine Learning, 2022
    
  
  2021