Abstract
Before talking about the security tools we must first think about what we should protect and how we should distinguish information that seems to be sensitive and identifiable among heterogeneous data that spread over several sources like Facebook, twitter and several other suppliers of big data. Thus, in this paper we proved a method of identifying sensitive information in mongo data store. In this paper we propose an innovative approach and its implementation as an expert system to achieve the automatic detection of the candidate attributes for fragmentation. Our approach is mainly based on semantic rules that determine which concepts have to be fragmented, and on a linguistic component that retrieves the attributes that semantically correspond to these concepts. Since attributes cannot be considered independently from each other we also address the challenging problem of the propagation of it among the whole mongo database. An important contribution of our approach is to provide a semantic modeling of sensitive data.
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Notes
- 1.
“JESS, the Rule Engine for the Java Platform,” http://www.jessrules.com.
- 2.
“WordNet: An Electronic Lexical Database,” http://wordnet.princeton.edu.
References
Atallah, M., Elmagarmid, A., Ibrahim, M., Bertino, E., Verykios, V.: Disclosure limitation of sensitive rules. In: Proceedings of the International Workshop on Knowledge and Data Engineering Exchange (KDEX), pp. 45–52 (1999)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl. Based. Syst. 10(5), 571–588 (2002)
Oliveira, S.R.M., Zaïane, O.R.: Protecting sensitive knowledge by data sanitization. In: Proceedings of the International Conference on Data Mining (ICDM), pp. 613–616 (2003)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-Diversity: privacy beyond K-anonymity. In: Proceedings of the International Conference on Data Engineering (ICDE), p. 24 (2006)
Li, N., Li, T., Venkatasubramanian, S.: t-Closeness: privacy beyond k-Anonymity and l-diversity. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 106–115 (2007)
Wang, E.T., Lee, G.: An efficient sanitization algorithm for balancing information privacy and knowledge discovery in association patterns mining. Data Knowl. Eng. (DKE) 65(3), 463–484 (2008)
Amiri, A.: Dare to share: protecting sensitive knowledge with data sanitization. Decis. Support Syst. 43(1), 181–191 (2007)
Atzori, M., Bonchi, F., Giannotti, F., Pedreschi, D.: Blocking anonymity threats raised by frequent itemset mining. In: Proceedings of the International Conference on Data Mining (ICDM), pp. 561–564 (2005)
Wang, Z., Wang, W., Shi, W.: Blocking inference channels in frequent pattern sharing. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 1425–1429 (2007)
Sweeney, L.: Datafly: a system for providing anonymity in medical data. In: Proceedings of the International Conference on Database Security (DBSec), pp. 356–381 (1997)
Beckwith, B.A., Mahaadevan, R., Balis, U.J., Kuo, F.: Development and evaluation of an open source software tool for deidentification of pathology reports. BMC Med. Inform. Decis. Mak. 6(12), 176–186 (2006)
Gardner, J., Xiong, L.: HIDE: an integrated system for health information DE-identification. In: Proceedings of the International Symposium on Computer-Based Medical Systems (CBMS), pp. 254–259 (2008)
Chakaravarthy, V.T., Gupta, H., Roy, P., Mohania, M.K.: Efficient techniques for document sanitization. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 843–852 (2008)
Datamasker. http://www.datamasker.com
Camouflage. http://www.datamasking.com
Solix. http://www.solix.com
Datavantage Globa. http://www.datavantage.com
Pse Data Security. http://www.psedatasecurity.com
Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 217–228 (2005)
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Heni, H., Gargouri, F. (2020). Towards an Automatic Detection of Sensitive Information in Mongo Database. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_13
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