---
# System prepended metadata

title: Untitled

---

### Papers (Please sign up for presentation [here](https://docs.google.com/spreadsheets/d/1GPbWKpnKutALnCCEBFBn1HyshOLH8oInoGSfOl7_p9M/edit?usp=sharing))

Privacy Attacks against Machine Learning

1. [Reconstructing Training Data with Informed Adversaries](https://arxiv.org/pdf/2201.04845.pdf)
2. [Extracting Training Data from Large Language Models](https://www.usenix.org/system/files/sec21-carlini-extracting.pdf)
3. [Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations](https://dl.acm.org/doi/pdf/10.1145/3243734.3243834)
4. [Counterfactual Memorization in Neural Language Models](https://arxiv.org/pdf/2112.12938)
5. [Formalizing and Estimating Distribution Inference Risks](https://arxiv.org/pdf/2109.06024.pdf)
6. [Enhanced Membership Inference Attacks against Machine Learning Models](https://arxiv.org/pdf/2111.09679.pdf)
7. [Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture](https://arxiv.org/pdf/2110.08324.pdf)
8. [Is Private Learning Possible with Instance Encoding?](https://arxiv.org/pdf/2011.05315.pdf)
9. [Submix: Practical Private Prediction For Large-scale Language Models](https://arxiv.org/pdf/2201.00971.pdf)
10. [Composition Attacks and Auxiliary Information in Data Privacy](https://arxiv.org/pdf/0803.0032)
11. [StolenEncoder: Stealing Pre-trained Encoders](https://arxiv.org/pdf/2201.05889.pdf)

Machine Learning Security (Instead of Privacy)

12. [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://www.usenix.org/system/files/sec20summer_fang_prepub.pdf)
13. [Spinning Language Models for Propaganda-As-A-Service](https://arxiv.org/pdf/2112.05224.pdf)
14. [Blind Backdoors in Deep Learning Models](https://arxiv.org/pdf/2005.03823.pdf)

Differential Privacy Theory

15. [Renyi Differential Privacy](https://arxiv.org/pdf/1702.07476.pdf)
16. [Numerical Composition of Differential Privacy](https://arxiv.org/pdf/2106.02848.pdf)
17. [Differentially Private Combinatorial Optimization](https://epubs.siam.org/doi/pdf/10.1137/1.9781611973075.90)
18. [Iterative Constructions and Private Data Release](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.649.1598&rep=rep1&type=pdf)
19. [On the Rényi Differential Privacy of the Shuffle Model](https://dl.acm.org/doi/pdf/10.1145/3460120.3484794)

Differential Privacy for Machine Learning

20. [Scalable Private Learning With PATE](https://arxiv.org/pdf/1802.08908.pdf)
21. [DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy](https://arxiv.org/pdf/2110.08557.pdf)
22. [Hyperparameter Tuning with Renyi Differential Privacy](https://arxiv.org/pdf/2110.03620.pdf)
23. [Benchmarking Differential Privacy and Federated Learning for BERT Models](https://arxiv.org/pdf/2106.13973.pdf)
24. [Public Data-Assisted Mirror Descent for Private Model Training](https://arxiv.org/pdf/2112.00193)
25. [Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning](https://arxiv.org/pdf/2101.04535.pdf)
26. [The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection](https://arxiv.org/pdf/2111.04906)
27. [Differentially private fine-tuning of language models](https://arxiv.org/pdf/2110.06500)
28. [Large language models can be strong differentially private learners](https://arxiv.org/pdf/2110.05679)
29. [Large Scale Private Learning via Low-rank Reparametrization](https://arxiv.org/pdf/2106.09352.pdf)

Differential Privacy and Cryptography

30. [Strengthening Order Preserving Encryption with Differential Privacy](https://arxiv.org/pdf/2009.05679.pdf)
31. [Shrinkwrap: Efficient SQL Query Processing in
Differentially Private Data Federations](https://par.nsf.gov/servlets/purl/10223658)
32. [Differentially Private Oblivious RAM](https://arxiv.org/pdf/1601.03378.pdf)

Privacy and Systems

33. [Veil: Private Browsing Semantics Without Browser-side Assistance](https://frankwang.org/files/papers/wang-veil.pdf)
34. [εpsolute: Efficiently Querying Databases While Providing Differential Privacy](https://arxiv.org/pdf/1706.01552.pdf)
35. [Packet scheduling with optional client privacy](https://www.cis.upenn.edu/~sga001/papers/ifs-ccs21.pdf)
36. [Data Privacy in Trigger-Action Systems](https://pages.cs.wisc.edu/~yc/assets/pdf/etap.pdf)
37. [εKTELO A Framework for Defining Differentially Private Computations](https://dl.acm.org/doi/pdf/10.1145/3362032)
38. [PrivateSQL: a differentially private SQL query engine](http://www.vldb.org/pvldb/vol12/p1371-kotsogiannis.pdf)

Other General Privacy

39. [Privacy Engineering Meets Software Engineering. On the Challenges of Engineering Privacy By Design](https://arxiv.org/pdf/2007.08613.pdf)
40. [Towards formalizing the GDPR’s notion of singling out](https://www.pnas.org/content/pnas/117/15/8344.full.pdf)
41. [Differential privacy: An economic method for choosing epsilon](https://arxiv.org/pdf/1402.3329)
42. [Privacy Implications of Shuffling](https://arxiv.org/pdf/2106.06603.pdf)
43. [Causally Constrained Data Synthesis for Private Data Release](https://arxiv.org/pdf/2105.13144.pdf)
44. [DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy](https://arxiv.org/pdf/2103.15942.pdf)
45. [Kamino: Constraint-Aware Differentially Private Data Synthesis](http://vldb.org/pvldb/vol14/p1886-ge.pdf)
