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Cynthia Dwork and Aaron Roth, The Algorithmic Foundations of Differential Privacy Foundations and Trends® in Theoretical Computer Science 9.3–4 (2014): 211-407.
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N. Rothblum, and Salil P. Vadhan, On the complexity of differentially private data release: efficient algorithms and hardness results Proceedings of the forty-first annual ACM symposium on Theory of computing. 2009.
Stanley L. Warner, Randomized response: a survey technique for eliminating evasive answer bias Journal of the American Statistical Association 60.309 (1965): 63-69.
Dong J, Roth A, Su W J. Gaussian differential privacy Journal of the Royal Statistical Society Series B: Statistical Methodology, 2022, 84(1): 3-37.
*Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith, * Smooth Sensitivity and Sampling in Private Data Analysis. Roceedings of the thirty-ninth annual ACM symposium on Theory of computing. 2007: 75-84
Nissim, Kobbi, Sofya Raskhodnikova, and Adam Smith, Smooth sensitivity and sampling in private data analysis. Proceedings of the thirty-ninth annual ACM symposium on Theory of computing. 2007.
Bun, Mark, Thomas Steinke, and Jonathan Ullman, Make up your mind: The price of online queries in differential privacy. Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, 2017.
Feldman, Vitaly, and Thomas Steinke, Generalization for adaptively-chosen estimators via stable median Conference on learning theory. PMLR, 2017.
Mironov, Ilya, Rényi differential privacy. 2017 IEEE 30th computer security foundations symposium (CSF). IEEE, 2017.
Dwork, Cynthia, and Guy N. Rothblum, Concentrated differential privacy.
Bun, Mark, and Thomas Steinke, Concentrated differential privacy: Simplifications, extensions, and lower bounds. Theory of Cryptography: 14th International Conference, TCC 2016-B, Beijing, China, October 31-November 3, 2016, Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016.
Bun, Mark, et al, Composable and versatile privacy via truncated cdp. Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing. 2018.
Evfimievski A, Gehrke J, Srikant R. Limiting privacy breaches in privacy preserving data mining, Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 2003: 211-222.
Erlingsson Ú, Pihur V, Korolova A. Rappor: Randomized aggregatable privacy-preserving ordinal response, Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. 2014: 1054-1067.
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith, What can we learn privately?, SIAM Journal on Computing, vol. 40, no. 3, pp. 793–826, 2011.
M. E. Gursoy, A. Tamersoy, S. Truex, W. Wei, and L. Liu, Secure and utility-aware data collection with condensed local differential privacy, IEEE Trans. on Dependable and Secure Comput., pp. 1–13, 2019
T. Murakami and Y. Kawamoto, Utility-optimized local differential privacy mechanisms for distribution estimation, in USENIX Security Symposium, 2019, pp. 1877–1894.
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, Our data, ourselves: Privacy via distributed noise generation, in Theory and Applications of Cryptographic Techniques, 2006, pp. 486–503
Cheu A, Smith A, Ullman J, et al. Distributed differential privacy via shuffling, Advances in Cryptology–EUROCRYPT 2019: 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Darmstadt, Germany, May 19–23, 2019, Proceedings, Part I 38. Springer International Publishing, 2019: 375-403.
B. Avent, A. Korolova, D. Zeber, T. Hovden, and B. Livshits, BLENDER: Enabling local search with a hybrid differential privacy model, USENIX Security Symposium, 2017, pp. 747–764
Y. NIE, W. Yang, L. Huang, X. Xie, Z. Zhao, and S. Wang, A utility-optimized framework for personalized private histogram estimation, IEEE Trans. Knowl. Data Eng., vol. 31, no. 4, pp. 655–669, 2019.
Shokri R, Shmatikov V. Privacy-Preserving Deep Learning Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 2015: 1310-1321.
Abadi M, Chu A, Goodfellow I, et al. Deep Learning with Differential Privacy Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016: 308-318.
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Cynthia Dwork and Aaron Roth, The Algorithmic Foundations of Differential Privacy Foundations and Trends® in Theoretical Computer Science 9.3–4 (2014): 211-407.
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N. Rothblum, and Salil P. Vadhan, On the complexity of differentially private data release: efficient algorithms and hardness results Proceedings of the forty-first annual ACM symposium on Theory of computing. 2009.
Stanley L. Warner, Randomized response: a survey technique for eliminating evasive answer bias Journal of the American Statistical Association 60.309 (1965): 63-69.
Dong J, Roth A, Su W J. Gaussian differential privacy Journal of the Royal Statistical Society Series B: Statistical Methodology, 2022, 84(1): 3-37.
*Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith, * Smooth Sensitivity and Sampling in Private Data Analysis. Roceedings of the thirty-ninth annual ACM symposium on Theory of computing. 2007: 75-84
Nissim, Kobbi, Sofya Raskhodnikova, and Adam Smith, Smooth sensitivity and sampling in private data analysis. Proceedings of the thirty-ninth annual ACM symposium on Theory of computing. 2007.
Bun, Mark, Thomas Steinke, and Jonathan Ullman, Make up your mind: The price of online queries in differential privacy. Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, 2017.
Feldman, Vitaly, and Thomas Steinke, Generalization for adaptively-chosen estimators via stable median Conference on learning theory. PMLR, 2017.
Mironov, Ilya, Rényi differential privacy. 2017 IEEE 30th computer security foundations symposium (CSF). IEEE, 2017.
Dwork, Cynthia, and Guy N. Rothblum, Concentrated differential privacy.
Bun, Mark, and Thomas Steinke, Concentrated differential privacy: Simplifications, extensions, and lower bounds. Theory of Cryptography: 14th International Conference, TCC 2016-B, Beijing, China, October 31-November 3, 2016, Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016.
Bun, Mark, et al, Composable and versatile privacy via truncated cdp. Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing. 2018.
Evfimievski A, Gehrke J, Srikant R. Limiting privacy breaches in privacy preserving data mining, Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 2003: 211-222.
Erlingsson Ú, Pihur V, Korolova A. Rappor: Randomized aggregatable privacy-preserving ordinal response, Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. 2014: 1054-1067.
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith, What can we learn privately?, SIAM Journal on Computing, vol. 40, no. 3, pp. 793–826, 2011.
M. E. Gursoy, A. Tamersoy, S. Truex, W. Wei, and L. Liu, Secure and utility-aware data collection with condensed local differential privacy, IEEE Trans. on Dependable and Secure Comput., pp. 1–13, 2019
T. Murakami and Y. Kawamoto, Utility-optimized local differential privacy mechanisms for distribution estimation, in USENIX Security Symposium, 2019, pp. 1877–1894.
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, Our data, ourselves: Privacy via distributed noise generation, in Theory and Applications of Cryptographic Techniques, 2006, pp. 486–503
Cheu A, Smith A, Ullman J, et al. Distributed differential privacy via shuffling, Advances in Cryptology–EUROCRYPT 2019: 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Darmstadt, Germany, May 19–23, 2019, Proceedings, Part I 38. Springer International Publishing, 2019: 375-403.
B. Avent, A. Korolova, D. Zeber, T. Hovden, and B. Livshits, BLENDER: Enabling local search with a hybrid differential privacy model, USENIX Security Symposium, 2017, pp. 747–764
Y. NIE, W. Yang, L. Huang, X. Xie, Z. Zhao, and S. Wang, A utility-optimized framework for personalized private histogram estimation, IEEE Trans. Knowl. Data Eng., vol. 31, no. 4, pp. 655–669, 2019.
Shokri R, Shmatikov V. Privacy-Preserving Deep Learning Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 2015: 1310-1321.
Abadi M, Chu A, Goodfellow I, et al. Deep Learning with Differential Privacy Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016: 308-318.
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