Bibliography#

[1]

Latanya Sweeney. Simple demographics often identify people uniquely. URL: https://dataprivacylab.org/projects/identifiability/.

[2]

Latanya Sweeney. K-anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):557–570, 2002. URL: https://doi.org/10.1142/S0218488502001648, arXiv:https://doi.org/10.1142/S0218488502001648, doi:10.1142/S0218488502001648.

[3]

Cynthia Dwork. Differential privacy. In Proceedings of the 33rd International Conference on Automata, Languages and Programming - Volume Part II, ICALP'06, 1–12. Berlin, Heidelberg, 2006. Springer-Verlag. URL: https://doi.org/10.1007/11787006_1, doi:10.1007/11787006_1.

[4]

Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Proceedings of the Third Conference on Theory of Cryptography, TCC'06, 265–284. Berlin, Heidelberg, 2006. Springer-Verlag. URL: https://doi.org/10.1007/11681878_14, doi:10.1007/11681878_14.

[5]

Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. Our data, ourselves: privacy via distributed noise generation. In Serge Vaudenay, editor, Advances in Cryptology - EUROCRYPT 2006, 486–503. Berlin, Heidelberg, 2006. Springer Berlin Heidelberg.

[6]

Frank D. McSherry. Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD '09, 19–30. New York, NY, USA, 2009. Association for Computing Machinery. URL: https://doi.org/10.1145/1559845.1559850, doi:10.1145/1559845.1559850.

[7]

Ilya Mironov. On significance of the least significant bits for differential privacy. In Proceedings of the 2012 ACM Conference on Computer and Communications Security, CCS '12, 650–661. New York, NY, USA, 2012. Association for Computing Machinery. URL: https://doi.org/10.1145/2382196.2382264, doi:10.1145/2382196.2382264.

[8]

Sílvia Casacuberta, Michael Shoemate, Salil Vadhan, and Connor Wagaman. Widespread underestimation of sensitivity in differentially private libraries and how to fix it. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS '22, 471–484. New York, NY, USA, 2022. Association for Computing Machinery. URL: https://doi.org/10.1145/3548606.3560708, doi:10.1145/3548606.3560708.

[9]

Borja Balle and Yu-Xiang Wang. Improving the Gaussian mechanism for differential privacy: analytical calibration and optimal denoising. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, 394–403. PMLR, 10–15 Jul 2018. URL: https://proceedings.mlr.press/v80/balle18a.html.

[10]

Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan. Boosting and differential privacy. In 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, volume, 51–60. 2010. doi:10.1109/FOCS.2010.12.

[11]

Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. Smooth sensitivity and sampling in private data analysis. In Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, STOC '07, 75–84. New York, NY, USA, 2007. Association for Computing Machinery. URL: https://doi.org/10.1145/1250790.1250803, doi:10.1145/1250790.1250803.

[12]

Cynthia Dwork and Jing Lei. Differential privacy and robust statistics. In Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, STOC '09, 371–380. New York, NY, USA, 2009. Association for Computing Machinery. URL: https://doi.org/10.1145/1536414.1536466, doi:10.1145/1536414.1536466.

[13]

Ilya Mironov. Renyi differential privacy. In Computer Security Foundations Symposium (CSF), 2017 IEEE 30th, 263–275. IEEE, 2017.

[14]

Mark Bun and Thomas Steinke. Concentrated differential privacy: simplifications, extensions, and lower bounds. In Theory of Cryptography Conference, 635–658. Springer, 2016.

[15]

Frank McSherry and Kunal Talwar. Mechanism design via differential privacy. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), volume, 94–103. 2007. doi:10.1109/FOCS.2007.66.

[16]

Cynthia Dwork, Aaron Roth, and others. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4):211–407, 2014.

[17]

Cynthia Dwork, Moni Naor, Omer Reingold, Guy N. Rothblum, and Salil Vadhan. On the complexity of differentially private data release: efficient algorithms and hardness results. In Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, STOC '09, 381–390. New York, NY, USA, 2009. Association for Computing Machinery. URL: https://doi.org/10.1145/1536414.1536467, doi:10.1145/1536414.1536467.

[18]

Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. Rappor: randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, CCS '14, 1054–1067. New York, NY, USA, 2014. Association for Computing Machinery. URL: https://doi.org/10.1145/2660267.2660348, doi:10.1145/2660267.2660348.

[19]

Stanley L. Warner. Randomized response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60(309):63–69, 1965. PMID: 12261830. URL: https://www.tandfonline.com/doi/abs/10.1080/01621459.1965.10480775, doi:10.1080/01621459.1965.10480775.

[20]

Tianhao Wang, Jeremiah Blocki, Ninghui Li, and Somesh Jha. Locally differentially private protocols for frequency estimation. In 26th USENIX Security Symposium (USENIX Security 17), 729–745. Vancouver, BC, August 2017. USENIX Association. URL: https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/wang-tianhao.