Differentially Private Group-by Data Releasing Algorithm
Resumo
Privacy concerns are growing fast because of data protection regulations around the world. Many works have built private algorithms avoiding sensitive information leakage through data publication. Differential privacy, based on formal definitions, is a strong guarantee for individual privacy and the cutting edge for designing private algorithms. This work proposes a differentially private group-by algorithm for data publication under the exponential mechanism. Our method publishes data groups according to a specified attribute while maintaining the desired privacy level and trustworthy utility results.
Palavras-chave:
differential privacy, privacy, group-by
Referências
Chen, R., Mohammed, N., Fung, B. C., Desai, B. C., and Xiong, L. (2011). Publishing setvalued data via differential privacy. Proceedings of the VLDB Endowment, 4(11):1087-1098. DOI: http://vldb.org/pvldb/vol4/p1087-chen.pdf
Dua, D. and Graff, C. (2017). UCI machine learning repository.
Dwork, C. (2008). Differential privacy: A survey of results. In Agrawal, M., Du, D., Duan, Z., and Li, A., editors, Theory and Applications of Models of Computation, pages 1–19, Berlin, Heidelberg. Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-540-79228-4_1
Dwork, C. (2011). Differential privacy. Encyclopedia of Cryptography and Security, pages 338–340. DOI: https://doi.org/10.1007/978-1-4419-5906-5_752
Harmanci, A. and Gerstein, M. (2016). Quantification of private information leakage from phenotype-genotype data: linking attacks. Nature methods, 13(3):251. DOI: https://doi.org/10.1038/nmeth.3746
McSherry, F. and Talwar, K. (2007). Mechanism design via differential privacy. In FOCS, volume 7, pages 94–103. DOI: https://doi.org/10.1109/focs.2007.66
McSherry, F. D. (2009). Privacy integrated queries: an extensible platform for privacy preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 19–30. ACM. DOI: https://doi.org/10.1145/1559845.1559850
Mendonça, A. L., Brito, F. T., Linhares, L. S., and Machado, J. C. (2017). Dipcoding: A differentially private approach for correlated data with clustering. In Proceedings of the 21st International Database Engineering & Applications Symposium, pages 291–297. ACM. DOI: https://doi.org/10.1145/3105831.3105861
Powers, D. M. (2011). Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. DOI: https://doi.org/10.9735/2229-3981
Dua, D. and Graff, C. (2017). UCI machine learning repository.
Dwork, C. (2008). Differential privacy: A survey of results. In Agrawal, M., Du, D., Duan, Z., and Li, A., editors, Theory and Applications of Models of Computation, pages 1–19, Berlin, Heidelberg. Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-540-79228-4_1
Dwork, C. (2011). Differential privacy. Encyclopedia of Cryptography and Security, pages 338–340. DOI: https://doi.org/10.1007/978-1-4419-5906-5_752
Harmanci, A. and Gerstein, M. (2016). Quantification of private information leakage from phenotype-genotype data: linking attacks. Nature methods, 13(3):251. DOI: https://doi.org/10.1038/nmeth.3746
McSherry, F. and Talwar, K. (2007). Mechanism design via differential privacy. In FOCS, volume 7, pages 94–103. DOI: https://doi.org/10.1109/focs.2007.66
McSherry, F. D. (2009). Privacy integrated queries: an extensible platform for privacy preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 19–30. ACM. DOI: https://doi.org/10.1145/1559845.1559850
Mendonça, A. L., Brito, F. T., Linhares, L. S., and Machado, J. C. (2017). Dipcoding: A differentially private approach for correlated data with clustering. In Proceedings of the 21st International Database Engineering & Applications Symposium, pages 291–297. ACM. DOI: https://doi.org/10.1145/3105831.3105861
Powers, D. M. (2011). Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. DOI: https://doi.org/10.9735/2229-3981
Publicado
07/10/2019
Como Citar
CHAVES, Iago; MACHADO, Javam.
Differentially Private Group-by Data Releasing Algorithm. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 271-276.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2019.8835.
