Differentially private partition selection

[1] G. Acs, C. Castelluccia, and R. Chen. Differentially private histogram publishing through lossy compression. In 2012 IEEE 12th International Conference on Data Mining, pages 1–10. IEEE, 2012.10.1109/ICDM.2012.80 Search in Google Scholar

[2] B. Balle and Y.-X. Wang. Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising. In International Conference on Machine Learning, pages 394–403. PMLR, 2018. Search in Google Scholar

[3] J. Bater, X. He, W. Ehrich, A. Machanavajjhala, and J. Rogers. Shrinkwrap: Differentially-private query processing in private data federations. arXiv preprint arXiv:1810.01816, 2018. Search in Google Scholar

[4] G. Cormode, C. Procopiuc, D. Srivastava, and T. T. Tran. Differentially private summaries for sparse data. In Proceedings of the 15th International Conference on Database Theory, pages 299–311, 2012.10.1145/2274576.2274608 Search in Google Scholar

[5] G. Cormode, M. Procopiuc, D. Srivastava, and T. T. Tran. Differentially private publication of sparse data. arXiv preprint arXiv:1103.0825, 2011.10.1145/2274576.2274608 Search in Google Scholar

[6] B. Ding, M. Winslett, J. Han, and Z. Li. Differentially private data cubes: optimizing noise sources and consistency. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pages 217–228, 2011.10.1145/1989323.1989347 Search in Google Scholar

[7] C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference, pages 265–284. Springer, 2006.10.1007/11681878_14 Search in Google Scholar

[8] C. Dwork, M. Naor, O. Reingold, G. N. Rothblum, and S. 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, pages 381–390, 2009.10.1145/1536414.1536467 Search in Google Scholar

[9] C. Dwork and A. Roth. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4):211–407, 2014.10.1561/0400000042 Search in Google Scholar

[10] P. Flajolet, É. Fusy, O. Gandouet, and F. Meunier. Hyper-loglog: the analysis of a near-optimal cardinality estimation algorithm. In Discrete Mathematics and Theoretical Computer Science, pages 137–156. Discrete Mathematics and Theoretical Computer Science, 2007.10.46298/dmtcs.3545 Search in Google Scholar

[11] Q. Geng, W. Ding, R. Guo, and S. Kumar. Tight analysis of privacy and utility tradeoff in approximate differential privacy. In International Conference on Artificial Intelligence and Statistics, pages 89–99, 2020. Search in Google Scholar

[12] Q. Geng and P. Viswanath. Optimal noise adding mechanisms for approximate differential privacy. IEEE Transactions on Information Theory, 62(2):952–969, Feb 2016.10.1109/TIT.2015.2504972 Search in Google Scholar

[13] A. Ghosh, T. Roughgarden, and M. Sundararajan. Universally utility-maximizing privacy mechanisms. SIAM Journal on Computing, 41(6):1673–1693, 2012.10.1137/09076828X Search in Google Scholar

[14] S. Gopi, P. Gulhane, J. Kulkarni, J. H. Shen, M. Shokouhi, and S. Yekhanin. Differentially private set union. arXiv preprint arXiv:2002.09745, 2020. Search in Google Scholar

[15] M. Hay, V. Rastogi, G. Miklau, and D. Suciu. Boosting the accuracy of differentially-private histograms through consistency. arXiv preprint arXiv:0904.0942, 2009. Search in Google Scholar

[16] N. Holohan, D. J. Leith, and O. Mason. Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof. Information Sciences, 305:256–268, 2015. Search in Google Scholar

[17] S. Inusah and T. J. Kozubowski. A discrete analogue of the laplace distribution. Journal of statistical planning and inference, 136(3):1090–1102, 2006.10.1016/j.jspi.2004.08.014 Search in Google Scholar

[18] N. Johnson, J. P. Near, and D. Song. Towards practical differential privacy for sql queries. Proceedings of the VLDB Endowment, 11(5):526–539, 2018.10.1145/3187009.3177733 Search in Google Scholar

[19] H. Kaplan, Y. Mansour, and U. Stemmer. The sparse vector technique, revisited. arXiv preprint arXiv:2010.00917, 2020. Search in Google Scholar

[20] D. Kifer and A. Machanavajjhala. No free lunch in data privacy. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pages 193–204, 2011.10.1145/1989323.1989345 Search in Google Scholar

[21] A. Korolova, K. Kenthapadi, N. Mishra, and A. Ntoulas. Releasing search queries and clicks privately. In Proceedings of the 18th international conference on World wide web, pages 171–180, 2009.10.1145/1526709.1526733 Search in Google Scholar

[22] I. Kotsogiannis, Y. Tao, X. He, M. Fanaeepour, A. Machanavajjhala, M. Hay, and G. Miklau. Privatesql: a differentially private sql query engine. Proceedings of the VLDB Endowment, 12(11):1371–1384, 2019. Search in Google Scholar

[23] J. Lee and C. W. Clifton. Top-k frequent itemsets via differentially private fp-trees. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 931–940, 2014.10.1145/2623330.2623723 Search in Google Scholar

[24] H. Li, L. Xiong, and X. Jiang. Differentially private synthesization of multi-dimensional data using copula functions. In Advances in database technology: proceedings. International conference on extending database technology, volume 2014, page 475. NIH Public Access, 2014. Search in Google Scholar

[25] M. Lyu, D. Su, and N. Li. Understanding the sparse vector technique for differential privacy. arXiv preprint arXiv:1603.01699, 2016. Search in Google Scholar

[26] R. J. Wilson, C. Y. Zhang, W. Lam, D. Desfontaines, D. Simmons-Marengo, and B. Gipson. Differentially private SQL with bounded user contribution. arXiv preprint arXiv:1909.01917, 2019. Search in Google Scholar

[27] X. Xiao, G. Wang, and J. Gehrke. Differential privacy via wavelet transforms. IEEE Transactions on knowledge and data engineering, 23(8):1200–1214, 2010.10.1109/TKDE.2010.247 Search in Google Scholar

[28] Y. Xiao, L. Xiong, L. Fan, and S. Goryczka. Dpcube: differentially private histogram release through multidimensional partitioning. arXiv preprint arXiv:1202.5358, 2012. Search in Google Scholar

[29] J. Xu, Z. Zhang, X. Xiao, Y. Yang, G. Yu, and M. Winslett. Differentially private histogram publication. The VLDB Journal, 22(6):797–822, 2013.10.1007/s00778-013-0309-y Search in Google Scholar

[30] J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao. Privbayes: Private data release via bayesian networks. ACM Transactions on Database Systems (TODS), 42(4):1–41, 2017. Search in Google Scholar

• Understanding Privacy-Related Advice on Stack Overflow

• Revisiting Identification Issues in GDPR ‘Right Of Access’ Policies: A Technical and Longitudinal Analysis

• Employees’ privacy perceptions: exploring the dimensionality and antecedents of personal data sensitivity and willingness to disclose

• Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases

• Analyzing the Feasibility and Generalizability of Fingerprinting Internet of Things Devices

• CoverDrop: Blowing the Whistle Through A News App

• Building a Privacy-Preserving Smart Camera System

• FP-Radar: Longitudinal Measurement and Early Detection of Browser Fingerprinting

• Are iPhones Really Better for Privacy? A Comparative Study of iOS and Android Apps

• How to prove any NP statement jointly? Efficient Distributed-prover Zero-Knowledge Protocols

• Editors’ Introduction

• PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics

• Who Knows I Like Jelly Beans? An Investigation Into Search Privacy

• SoK: Plausibly Deniable Storage

• d3p - A Python Package for Differentially-Private Probabilistic Programming

• Updatable Private Set Intersection

• Knowledge Cross-Distillation for Membership Privacy

• RegulaTor: A Straightforward Website Fingerprinting Defense

• Privacy-Preserving Positioning in Wi-Fi Fine Timing Measurement

• Efficient Set Membership Proofs using MPC-in-the-Head

• Checking Websites’ GDPR Consent Compliance for Marketing Emails

• Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction

• Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring

• User-Level Label Leakage from Gradients in Federated Learning

• Privacy-preserving training of tree ensembles over continuous data

• Differentially Private Simple Linear Regression

• Increasing Adoption of Tor Browser Using Informational and Planning Nudges