publication . Preprint . 2017

APEx: Accuracy-Aware Differentially Private Data Exploration

Ge, Chang; He, Xi; Ilyas, Ihab F.; Machanavajjhala, Ashwin;
Open Access English
  • Published: 29 Dec 2017
Abstract
Comment: Full version of the ACM SIGMOD 2019 paper
Subjects
free text keywords: Computer Science - Databases
Download from
33 references, page 1 of 3

[1] Simmetrics. https://github.com/Simmetrics/simmetrics.

[2] M. Abadi, A. Chu, I. J. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. Deep learning with di erential privacy. In SIGSAC, 2016.

[3] Z. Abedjan, J. Morcos, M. N. Gubanov, I. F. Ilyas, M. Stonebraker, P. Papotti, and M. Ouzzani. Dataxformer: Leveraging the web for semantic transformations. In CIDR, 2015. [OpenAIRE]

[4] K. Bellare, C. Curino, A. Machanavajjhala, P. Mika, M. Rahukar, and A. Sane. Woo: A scalable and multi-tenant platform for continuous knowledge base synthesis. In Proceedings of Very Large Data Bases (PVLDB) - Industrial Track, 2013. [OpenAIRE]

[5] S. Das, A. Doan, P. S. G. C., C. Gokhale, and P. Konda. The magellan data repository. https://sites.google.com/site/anhaidgroup/ projects/data.

[6] C. Dwork, F. McSherry, K. Nissim, and A. D. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, 2006.

[7] C. Dwork and A. Roth. The algorithmic foundations of di erential privacy. Found. Trends Theor. Comput. Sci., 2014.

[8] A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate record detection: A survey. IEEE Trans. Knowl. Data Eng., 2007. [OpenAIRE]

[9] U. Erlingsson, V. Pihur, and A. Korolova. Rappor: Randomized aggregatable privacy-preserving ordinal response. In CCS, 2014. [OpenAIRE]

[10] H. Galhardas, D. Florescu, D. E. Shasha, and E. Simon. AJAX: an extensible data cleaning tool. In SIGMOD, 2000. [OpenAIRE]

[11] A. Greenberg. Apple's `di erential privacy' is about collecting your data|but not your data. Wired, 2016.

[12] S. Haney, A. Machanavajjhala, J. Abowd, M. Graham, M. Kutzbach, and L. Vilhuber. Utility cost of formal privacy for releasing national employer-employee statistics. In SIGMOD, 2017.

[13] M. Hay, A. Machanavajjhala, G. Miklau, Y. Chen, and D. Zhang. Principled evaluation of di erentially private algorithms using dpbench. In SIGMOD, 2016.

[14] N. M. Johnson, J. P. Near, and D. X. Song. Practical di erential privacy for SQL queries using elastic sensitivity. CoRR, abs/1706.09479, 2017.

[15] S. Kandel, A. Paepcke, J. M. Hellerstein, and J. Heer. Wrangler: interactive visual speci cation of data transformation scripts. In CHI, 2011.

33 references, page 1 of 3
Abstract
Comment: Full version of the ACM SIGMOD 2019 paper
Subjects
free text keywords: Computer Science - Databases
Download from
33 references, page 1 of 3

[1] Simmetrics. https://github.com/Simmetrics/simmetrics.

[2] M. Abadi, A. Chu, I. J. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. Deep learning with di erential privacy. In SIGSAC, 2016.

[3] Z. Abedjan, J. Morcos, M. N. Gubanov, I. F. Ilyas, M. Stonebraker, P. Papotti, and M. Ouzzani. Dataxformer: Leveraging the web for semantic transformations. In CIDR, 2015. [OpenAIRE]

[4] K. Bellare, C. Curino, A. Machanavajjhala, P. Mika, M. Rahukar, and A. Sane. Woo: A scalable and multi-tenant platform for continuous knowledge base synthesis. In Proceedings of Very Large Data Bases (PVLDB) - Industrial Track, 2013. [OpenAIRE]

[5] S. Das, A. Doan, P. S. G. C., C. Gokhale, and P. Konda. The magellan data repository. https://sites.google.com/site/anhaidgroup/ projects/data.

[6] C. Dwork, F. McSherry, K. Nissim, and A. D. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, 2006.

[7] C. Dwork and A. Roth. The algorithmic foundations of di erential privacy. Found. Trends Theor. Comput. Sci., 2014.

[8] A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate record detection: A survey. IEEE Trans. Knowl. Data Eng., 2007. [OpenAIRE]

[9] U. Erlingsson, V. Pihur, and A. Korolova. Rappor: Randomized aggregatable privacy-preserving ordinal response. In CCS, 2014. [OpenAIRE]

[10] H. Galhardas, D. Florescu, D. E. Shasha, and E. Simon. AJAX: an extensible data cleaning tool. In SIGMOD, 2000. [OpenAIRE]

[11] A. Greenberg. Apple's `di erential privacy' is about collecting your data|but not your data. Wired, 2016.

[12] S. Haney, A. Machanavajjhala, J. Abowd, M. Graham, M. Kutzbach, and L. Vilhuber. Utility cost of formal privacy for releasing national employer-employee statistics. In SIGMOD, 2017.

[13] M. Hay, A. Machanavajjhala, G. Miklau, Y. Chen, and D. Zhang. Principled evaluation of di erentially private algorithms using dpbench. In SIGMOD, 2016.

[14] N. M. Johnson, J. P. Near, and D. X. Song. Practical di erential privacy for SQL queries using elastic sensitivity. CoRR, abs/1706.09479, 2017.

[15] S. Kandel, A. Paepcke, J. M. Hellerstein, and J. Heer. Wrangler: interactive visual speci cation of data transformation scripts. In CHI, 2011.

33 references, page 1 of 3
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