News

Schedule of presentation

Written on 26.10.2021 18:17 by Rui Wen

Update: We decide to cancel the presentation on 24.01, please check the new schedule.

-------------------------------------------------------------

 

Dear all,

After receiving your responses, we have arranged a schedule for you to give the presentations (see it at the end of this message).

Update: Next Monday is a holiday, there will be no seminar.

Thus, the presentation will start on 08.11.  Every Monday from 2 pm to 4 pm, we will have two presenters introduce their preferred papers.

See you next week. :)

Best,
Rui

 

-----------------------------------------------------------------

08.11:

1. Xinyue Shen, ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

2. Yiyong Liu, Membership Leakage in Label-Only Exposures

 

15.11:

3. Xiangyu Dong, Extracting Training Data from Large Language Models

4. Elisa Ebler, Privacy Risks of Securing Machine Learning Models against Adversarial Examples

 

22.11:

5. Kazi Fozle Azim Rabi, Practical Blind Membership Inference Attack via Differential Comparison

6. Zeyang Sha, Quantifying and Mitigating Privacy Risks of Contrastive Learning

 

29.11:

7. Yixin Wu, Overlearning Reveals Sensitive Attributes

8. Ziqing Yang, Auditing Data Provenance in Text-Generation Models

 

06.12:

9. Tanvi Ajay Gunjal, The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

10. Minxing Zhang, Membership Inference Attacks Against Recommender Systems

 

13.12:

11. Yiting Qu, Quantifying Privacy Leakage in Graph Embedding

12. Kavu Maithri Rao, The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks.

 

10.01:

13. Prajvi Saxena, Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

14. MANUELA CERON, When Machine Unlearning Jeopardizes Privacy

 

17.01:

15. Elisa Ebler, Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

16. Ankita Behura, Membership Inference Attacks Against Machine Learning Models

 


 

Privacy Policy | Legal Notice
If you encounter technical problems, please contact the administrators.