[8] Konstantinos Chatzikokolakis, Miguel E. Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi. Broadening the scope of differential privacy using metrics. In Emiliano De Cristofaro and Matthew K. Wright, editors, Privacy Enhancing Technologies - 13th International Symposium, PETs 2013, Bloomington, IN, USA, July 10-12, 2013. Proceedings, volume 7981 of Lecture Notes in Computer Science, pages 82-102. Springer, 2013.
[9] Jeremy M. Cohen, Elan Rosenfeld, and J. Zico Kolter. Certified adversarial robustness via randomized smoothing. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 1310-1320. PMLR, 2019.
[10] Li Deng. The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6):141-142, 2012.
[11] Xin Dong, Barbara De Salvo, Meng Li, Chiao Liu, Zhongnan Qu, HT Kung, and Ziyun Li. Splitnets: Designing neural architectures for efficient distributed computing on head-mounted systems. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12559-12569, 2022.
[12] Alexey Dosovitskiy and Thomas Brox. Inverting visual representations with convolutional networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 4829-4837. IEEE Computer Society, 2016.
[13] Flavio du Pin Calmon and Nadia Fawaz. Privacy against statistical inference. In 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012, Allerton Park & Retreat Center, Monticello, IL, USA, October 1-5, 2012, pages 1401-1408. IEEE, 2012.
[14] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006.
[15] Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2014.
[16] Liyue Fan. Image pixelization with differential privacy. In Florian Kerschbaum and Stefano Paraboschi, editors, Data and Applications Security and Privacy XXXII - 32nd Annual IFIP WG 11.3 Conference, DBSec 2018, Bergamo, Italy, July 16-18, 2018, Proceedings, volume 10980 of Lecture Notes in Computer Science, pages 148-162. Springer, 2018.
[17] Liyue Fan. Differential privacy for image publication. In Theory and Practice of Differential Privacy (TPDP) Workshop, volume 1, page 6, 2019.
[18] Farhad Farokhi and Henrik Sandberg. Fisher information as a measure of privacy: Preserving privacy of households with smart meters using batteries. IEEE Trans. Smart Grid, 9(5):4726-4734, 2018.
[19] Richard D Gill and Boris Y Levit. Applications of the van trees inequality: a bayesian cramer-ral bound. Bernoulli, pages 59–79, 1995.
[20] Chuan Guo, Brian Karrer, Kamalika Chaudhuri, and Laurens van der Maaten. Bounding training data reconstruction in private (deep) learning. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato, editors, International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 8056-8071. PMLR, 2022.
[21] Chuan Guo, Alexandre Sablayrolles, and Maziar Sanjabi. Analyzing privacy leakage in machine learning via multiple hypothesis testing: A lesson from fano. arXiv preprint arXiv:2210.13662, 2022.
[22] Awni Hannun, Chuan Guo, and Laurens van der Maaten. Measuring data leakage in machine-learning models with fisher information. In Uncertainty in Artificial Intelligence, pages 760-770. PMLR, 2021.