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English Information

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Authors
# Name
1 Lucas Bertelli(lucasbm@id.uff.br)
2 Victor Ströele(victor.stroele@ice.ufjf.br)
3 Javam Machado(javam.machado@lsbd.ufc.br)
4 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 Backstrom, L., Dwork, C., and Kleinberg, J. (2007). Wherefore art thou r3579x? anonymi- zed social networks, hidden patterns, and structural steganography. In WWW’07, pages 181–190.
2 de Lourdes Maia Silva, M., Chaves, I. C., and Machado, J. C. (2021). Private reverse top-k algorithms applied on public data of COVID-19 in the state of ceará. J. Inf. Data Manag., 12(5).
3 de Oliveira, D., Neto, E. R. D., et al. (2019). Um estudo comparativo de mecanismos de privacidade diferencial sobre um dataset de ocorrências do ZIKV no brasil. In Proc. of the 34th SBBD, pages 253–258. SBC
4 Duggan, J., Elmore, A. J., Stonebraker, M., Balazinska, M., Howe, B., Kepner, J., Madden, S., Maier, D., Mattson, T., and Zdonik, S. (2015). The bigdawg polystore system. ACM Sigmod Record, 44(2):11–16
5 Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265–284. Springer.
6 Dwork, C., Roth, A., et al. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4):211–407.
7 Erlingsson, Ú., Pihur, V., and Korolova, A. (2014). Rappor: Randomized aggregatable privacy-preserving ordinal response. In SIGSAC’14, pages 1054–1067.
8 Ge, C., He, X., Ilyas, I. F., and Machanavajjhala, A. (2019). Apex: Accuracy-aware diffe- rentially private data exploration. In SIGMOD ’19, pages 177–194
9 Johnson, N., Near, J. P., and Song, D. (2018). Towards practical differential privacy for sql queries. Proceedings of the VLDB Endowment, 11(5):526–539
10 Kraska, T., Stonebraker, M., Brodie, M. L., Servan-Schreiber, S., and Weitzner, D. J. (2019). Schengendb: A data protection database proposal. In Poly’19, volume 11721, pages 24– 38. Springer
11 Machanavajjhala, A., Kifer, D., Gehrke, J., and Venkitasubramaniam, M. (2007). l-diversity: Privacy beyond k-anonymity. ACM TKDD, 1(1):3–es
12 McSherry, F. D. (2009). Privacy integrated queries: an extensible platform for privacy- preserving data analysis. In SIGMOD’09, pages 19–30
13 Mendes, Y., de Oliveira, D., and Ströele, V. (2020). Polyflow: a polystore-compliant me- chanism to provide interoperability to heterogeneous provenance graphs. J. Inf. Data Manag., 11(3)
14 Nargesian, F., Zhu, E., Miller, R. J., Pu, K. Q., and Arocena, P. C. (2019). Data lake management: Challenges and opportunities. Proc. VLDB Endow., 12(12):1986–1989.
15 Proserpio, D., Goldberg, S., and McSherry, F. (2014). Calibrating data to sensitivity in private data analysis: A platform for differentially-private analysis of weighted datasets. PVLDB, 7(8):637–648.
16 Ramos, L. F. M. and Silva, J. a. M. C. (2019). Privacy and data protection concerns regarding the use of blockchains in smart cities. In ICEGOV’2019, page 342–347, Melbourne, Australia. ACM.
17 Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):557–570
18 Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60(309):63–69.