Fetching the paper…
Reading the bibliography…
Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data.
S. D. Bay, D. Kibler, M. J. Pazzani, and P. Smyth, “The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation,” ACM SIGKDD Explorations Newsletter , vol. 2, no. 2, pp. 81–85, 2000
2000
Earlier work this paper cites.
I. Stoica, R. Morris, D. Liben-Nowell, D. R. Karger, M. F. Kaashoek, F. Dabek, and H. Balakrishnan, “Chord: A Scalable Peer-to-Peer Lookup Protocol for Internet Applications,” IEEE/ACM Transactions on Networking , vol. 11, no. 1, pp. 17–32, 2003
2003
Earlier work this paper cites.
M. Hay, C. Li, G. Miklau, and D. Jensen, “Accurate Estimation of the Degree Distribution of Private Networks,” in IEEE International Conference on Data Mining , 2009
2009
Earlier work this paper cites.
A. B. Tsybakov, “Introduction to Nonparametric Estimation,” 2009
2009
Earlier work this paper cites.
Mathematics State Exchange User ‘user13888’, “What is the relationship of ℒ 1 \mathcal{L}_{1} (total variation) distance to hypothesis testing?” Mathematics Stack Exchange, https://math.stackexchange.com/q/72730
2011
Earlier work this paper cites.
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart, “Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing,” in USENIX Security Symposium , 2014
2014
Earlier work this paper cites.
D. Kifer and A. Machanavajjhala, “Pufferfish: A Framework for Mathematical Privacy Definitions,” ACM Transactions on Database Systems (TODS) , 2014
2014
Earlier work this paper cites.
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici, “Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers,” International Journal of Security and Networks , vol. 10, no. 3, pp. 137–150, 2015
2015
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership Inference Attacks against Machine Learning Models,” in IEEE Symposium on Security and Privacy , 2017
2017
Earlier work this paper cites.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep Sets,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2017
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” in International Conference on Learning Representations , 2017
2017
Earlier work this paper cites.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting,” in IEEE Computer Security Foundations Symposium , 2018
2018
Earlier work this paper cites.
K. Ganju, Q. Wang, W. Yang, C. A. Gunter, and N. Borisov, “Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations,” in ACM Conference on Computer and Communications Security , 2018
2018
Cited alongside, same era.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Large-scale CelebFaces Attributes (CelebA) Dataset,” 2018
2018
Cited alongside, same era.
B. Wang and N. Z. Gong, “Stealing Hyperparameters in Machine Learning,” in IEEE Symposium on Security and Privacy , 2018
2018
Cited alongside, same era.
Center for Applied Internet Data Analysis, “The CAIDA UCSD Anonymized Internet Traces,” https://www.caida.org/data/passive/passive˙dataset.xml
2018
Cited alongside, same era.
D. Gopinath, H. Converse, C. Pasareanu, and A. Taly, “Property Inference for Deep Neural Networks,” in IEEE/ACM International Conference on Automated Software Engineering , 2019
P. Kairouz et al. , “Advances and open problems in federated learning,” Foundations and Trends in Machine Learning , vol. 14, no. 1–2, pp. 1–210, 2021
2021
Closest in time.
M. Chase, E. Ghosh, and S. Mahloujifar, “Property Inference from Poisoning,” arXiv:2101.11073 , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
W. Zhang, S. Tople, and O. Ohrimenko, “Leakage of Dataset Properties in Multi-Party Machine Learning,” in USENIX Security Symposium , 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
S. S. Halabi, L. M. Prevedello, J. Kalpathy-Cramer, A. B. Mamonov, A. Bilbily, M. Cicero, I. Pan, L. A. Pereira, R. T. Sousa, N. Abdala et al. , “The RSNA Pediatric Bone Age Machine Learning Challenge,” Radiology , vol. 290, no. 2, pp. 498–503, 2019
2019
Cited alongside, same era.
D. Desfontaines and B. Pejó, “SoK: Differential privacies,” in Privacy Enhancing Technologies Symposium , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated Learning: Challenges, Methods, and Future Directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, 2020
2020
Cited alongside, same era.
K. Wang, Z. Shen, C. Huang, C.-H. Wu, Y. Dong, and A. Kanakia, “Microsoft Academic Graph: When experts are not enough,” Quantitative Science Studies , vol. 1, no. 1, pp. 396–413, 2020
2020
Cited alongside, same era.
J. Zhou, A. M. Xu, Zhiying amd Rush, and M. Yu, “Automating Botnet Detection with Graph Neural Networks,” AutoML for Networking and Systems Workshop of MLSys 2020 Conference , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Closest in time.
P. Maini, M. Yaghini, and N. Papernot, “Dataset Inference: Ownership Resolution in Machine Learning,” in International Conference on Learning Representations , 2021
2021
Closest in time.
X. Xu, Q. Wang, H. Li, N. Borisov, C. A. Gunter, and B. Li, “Detecting AI Trojans Using Meta Neural Analysis,” in IEEE Symposium on Security and Privacy , 2021
2021
Closest in time.
D. Pasquini, G. Ateniese, and M. Bernaschi, “Unleashing the Tiger: Inference Attacks on Split Learning,” in ACM SIGSAC Conference on Computer and Communications Security , 2021
2021
Closest in time.
Z. Zhang, M. Chen, M. Backes, Y. Shen, and Y. Zhang, “Inference Attacks Against Graph Neural Networks,” in USENIX Security Symposium , vol. 2022, 2021
2021
Closest in time.
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel, “Extracting Training Data from Large Language Models,” in USENIX Security Symposium , 2021
2021
Closest in time.
2021
Closest in time.
B. Kulynych, M. Yaghini, G. Cherubin, M. Veale, and C. Troncoso, “Disparate Vulnerability to Membership Inference Attacks,” in Privacy-Enhancing Technologies Symposium , 2022
2022
Closest in time.