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Authors
# Name
1 Javam Machado(javam.machado@lsbd.ufc.br)
2 Victor de Farias(victor.farias@lsbd.ufc.br)

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Reference
# Reference
1 Blake, C. L. and Merz, C. J. (1998). Uci repository of machine learning databases.
2 Brasil (2018). Lei geral de proteção de dados pessoais (lgpd).
3 Cavalcante, D. M., de Farias, V. A., Sousa, F. R., Paula, M. R. P., Machado, J. C., and de Souza, J. N. (2018). Popring: A popularity-aware replica placement for distributed key-value store. CLOSER, 2018:440–447.
4 Commission, E. (2018). 2018 reform of eu data protection rules.
5 de Farias, V. A. E., Brito, F. T., Flynn, C., Machado, J. C., Majumdar, S., and Srivastava, D. (2020). Local dampening: Differential privacy for non-numeric queries via local sensitivity. Proc. VLDB Endow., 14(4):521–533.
6 Dwork, C. (2011). Differential privacy. Encyclopedia of Cryptography and Security, pages 338–340
7 Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., and Naor, M. (2006a). Our data, ourselves: Privacy via distributed noise generation. In Annual International Confe- rence on the Theory and Applications of Cryptographic Techniques, pages 486–503. Springer.
8 Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006b). Calibrating noise to sensi- tivity in private data analysis. In Theory of cryptography conference, pages 265–284. Springer.
9 Farias, V. (2021). Local dampening: differential privacy for non-numeric queries via local sensitivity. PhD thesis, Universidade Federal do Ceará.
10 Farias, V., Pinheiro, P., Sousa, F., Gomes, J., and Machado, J. (2017). Online performance modeling for nosql databases using extreme learning machines. In Anais do XXXII Simpósio Brasileiro de Bancos de Dados, pages 276–281, Porto Alegre, RS, Brasil. SBC.
11 Farias, V. A., Brito, F. T., Flynn, C., Machado, J. C., Majumdar, S., and Srivastava, D. (2023). Local dampening: Differential privacy for non-numeric queries via local sen- sitivity. The VLDB Journal, pages 1–24.
12 Farias, V. A., Sousa, F. R., Maia, J. G. R., Gomes, J. P. P., and Machado, J. C. (2018). Regression based performance modeling and provisioning for nosql cloud databases. Future Generation Computer Systems, 79:72–81.
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16 Lima, M. I., de Farias, V. A., Praciano, F. D., and Machado, J. C. (2018). Workload-aware parameter selection and performance prediction for in-memory databases. In Anais do XXXIII Simpósio Brasileiro de Banco de Dados, pages 169–180. SBC.
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18 Machanavajjhala, A., He, X., and Hay, M. (2017). Differential privacy in the wild: A tutorial on current practices & open challenges. In Proceedings of the 2017 ACM SIGMOD International Conference on Management of data, pages 1727–1730. ACM.
19 Manton, K. G. (2010). National long-term care survey: 1982, 1984, 1989, 1994, 1999, and 2004. Inter-university Consortium for Political and Social Research.
20 McKenna, R. and Sheldon, D. R. (2020). Permute-and-flip: A new mechanism for diffe- rentially private selection. Advances in Neural Information Processing Systems, 33.
21 Nissim, K., Raskhodnikova, S., and Smith, A. (2007). Smooth sensitivity and sampling in private data analysis. In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pages 75–84. ACM.
22 Paula, M. R. P., Rodrigues, E., Farias, V. A., Sousa, F. R., and Machado, J. C. (2017). Bacos: A dynamic load balancing strategy for cloud object storage. In Anais do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.
23 Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1):81–106.
24 Series, I. P. U. M. (2015). Version 6.0. Minneapolis: University of.
25 Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., and Xiao, X. (2015). Private release of graph statistics using ladder functions. In Proceedings of the 2015 ACM SIGMOD international conference on management of data, pages 731–745. ACM.