Abstract
Today, with the advances of information technology, individual people and organizations can obtain and process data from different sources. It is critical to ensure data integrity so that effective decisions can be made based on these data. An important component of any solution for assessing data integrity is represented by techniques and tools to evaluate the trustworthiness of data provenance. However, few efforts have been devoted to investigate approaches for assessing how trusted the data are, based in turn on an assessment of the data sources and intermediaries. To bridge this gap, we propose a data provenance trust model which takes into account various factors that may affect the trustworthiness and, based on these factors, assigns trust scores to both data and data providers. Such trust scores represent key information based on which data users may decide whether to use the data and for what purposes.
The authors have been partially supported by AFOSR grant FA9550-07-1-0041 “Systematic Control and Management of Data Integrity, Quality and Provenance for Command and Control Applications”.
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References
Batini, C., Scannapieco, M.: Data quality: Concepts, methodologies and techniques. Springer, Heidelberg (2006)
Benjelloun, O., Sarma, A.D., Halevy, A.Y., Theobald, M., Widom, J.: Databases with uncertainty and lineage. VLDB Journal 17(2), 243–264 (2008)
Benjelloun, O., Sarma, A.D., Hayworth, C., Widom, J.: An introduction to uldbs and the trio system. IEEE Data Eng. Bull. 29(1), 5–16 (2006)
Buneman, P., Khanna, S., Tan, W.C.: Why and where: A characterization of data provenance. In: ICDT, pp. 316–330 (2001)
Byun, J.-W., Kamra, A., Bertino, E., Li, N.: Efficient k -anonymization using clustering techniques. In: International Conference on Database Systems for Advanced Applications, pp. 188–200 (2007)
Greenwood, M., Goble, C., Stevens, R., Zhao, J., Addis, M., Marvin, D., Moreau, L., Oinn, T.: Provenance of e-science experiments - experience from bioinformatics. In: UK OST e-Science second All Hands Meeting (2003)
Han, J., Kamber, M.: Data mining: Concepts and techniques. Morgan Kaufmann, San Francisco (2001)
Lanter, D.P.: Design of a lineage-based meta-data base for gis. Cartography and Geographic Information Systems 18, 255–261 (1991)
Scannapieco, M., Figotin, I., Bertino, E., Elmagarmid, A.K.: Privacy preserving schema and data matching. In: SIGMOD Conference, pp. 653–664 (2007)
Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance techniques. Technical Report (2007)
Song, S., Hwang, K., Zhou, R., Kwok, Y.-K.: Trusted p2p transactions with fuzzy reputation aggregation. IEEE Internet Computing 9(6), 24–34 (2005)
Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. J. ACM 21(1), 168–173 (1974)
Xiong, L., Liu, L.: Peertrust: Supporting reputation-based trust for peer-to-peer electronic communities. IEEE Trans. Knowl. Data Eng. 16(7), 843–857 (2004)
Yin, X., Han, J., Yu, P.S.: Truth discovery with multiple conflicting information providers on the web. In: ACM SIGKDD, pp. 1048–1052 (2007)
Zimmermann, P.: Pretty Good Privacy Users Guide. Distributed with the PGP software, vol. I & II (June 14, 1993)
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Dai, C., Lin, D., Bertino, E., Kantarcioglu, M. (2008). An Approach to Evaluate Data Trustworthiness Based on Data Provenance. In: Jonker, W., Petković, M. (eds) Secure Data Management. SDM 2008. Lecture Notes in Computer Science, vol 5159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85259-9_6
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DOI: https://doi.org/10.1007/978-3-540-85259-9_6
Publisher Name: Springer, Berlin, Heidelberg
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