Abstract
Data aggregation is a key element in many applications that draw insights from data analytics, such as medical research, smart metering, recommendation systems and real-time marketing. In general, data is gathered from several sources, processed, and publicly released for data analysis. Since the considered data might contain personal and sensitive information, special handling of private data is required.
In this paper, we present a novel distributed privacy-preserving data aggregation protocol, called ADiPA. It relies on anonymization techniques for protecting personal data, such as k-anonymity, l-diversity and t-closeness. Its purpose is to allow a set of entities to derive aggregate results from data tables that are partitioned across these entities in a fully decentralized manner while preserving the privacy of their individual sensitive inputs. ADiPA neither relies on a trusted third party nor on cryptographic techniques. The protocol performs accurate aggregation when communication links and nodes do not fail.
This work is supported by the BMBF (PMARS Programme) and the DAAD (German-Arab Transformation Partnership).
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References
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Anonymizing tables. In: Eiter, T., Libkin, L. (eds.) ICDT 2005. LNCS, vol. 3363, pp. 246–258. Springer, Heidelberg (2005)
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Approximation algorithms for k-anonymity. In: Proceedings of the International Conference on Database Theory (ICDT 2005), November 2005
Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, pp. 217–228. IEEE Computer Society, Washington, DC (2005)
Benkaouz, Y., Erradi, M.: A distributed protocol for privacy preserving aggregation. In: Gramoli, V., Guerraoui, R. (eds.) NETYS 2013. LNCS, vol. 7853, pp. 221–232. Springer, Heidelberg (2013)
Ciriani, V., De Capitani di Vimercati, S., Foresti, S., Samarati, P.: k-anonymity. In: Yu, T., Jajodia, S. (eds.) Secure Data Management in Decentralized Systems. Advances in Information Security, vol. 33, pp. 323–353. Springer, US (2007)
Di Vimercati, S.D.C., Foresti, S., Livraga, G., Samarati, P.: Data privacy: definitions and techniques. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 20(06), 793–817 (2012)
Fung, B.C.M., Wang, K., Yu, P.S.: Top-down specialization for information and privacy preservation. In: Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, pp. 205–216. IEEE Computer Society, Washington, DC (2005)
Golle, P.: Revisiting the uniqueness of simple demographics in the us population. In: WPES 2006, Alexandria, Virginia, USA, October 30, 2006
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 279–288. ACM, New York (2002)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: Efficient full-domain k-anonymity. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, SIGMOD 2005, pp. 49–60. ACM, New York (2005)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: ICDE 2007, IEEE 23rd International Conference on Data Engineering, 2007, pp. 106–115, April 2007
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1), 3 (2007)
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2004, pp. 223–228. ACM, New York (2004)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical report, CMU, SRI (1998)
Sweeney, L.: Uniqueness of simple demographics in the U.S. population. Technical report, Carnegie Mellon University, Laboratory for International Data Privacy (2000)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)
Sweeney, L.: Guaranteeing anonymity when sharing medical data, the datafly system. In: Journal of the American Medical Informatics Association. Hanley and Belfus Inc, Washington, DC (1997)
Winkler, W.E.: Using simulated annealing for k-anonymity. Technical report, Statistical Research Division, U.S. Bureau of the Census, Washington D.C. (2002)
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Benkaouz, Y., Erradi, M., Freisleben, B. (2015). Distributed Privacy-Preserving Data Aggregation via Anonymization. In: Bouajjani, A., Fauconnier, H. (eds) Networked Systems . NETYS 2015. Lecture Notes in Computer Science(), vol 9466. Springer, Cham. https://doi.org/10.1007/978-3-319-26850-7_7
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