Abstract
In this paper, we address the issue of privacy preservation in the context of publishing social network data. The individuals in published social networks are typically anonymous; however, an adversary may be able to combine the released anonymous social network data with publicly available non-sensitive information to re-identify the individuals in a social network. In this paper, we consider the case that an adversary can query such publicly available databases with description logic(DL) concepts. To address the privacy issue, we utilize social position analysis techniques to determine the indiscernibility of individuals in a social network. Social position analysis attempts to find individuals that occupy the same position in a social network based on the pattern of their relationships to other actors. Recently, it was shown that social positions can be characterized by modal logics; thus, individuals occupying the same social position will satisfy the same set of modal formulas. Since DL has a close correspondences with modal logic, individuals occupying the same social position can not be distinguished by the knowledge expressed in DL formalisms. By partitioning a set of individuals into indiscernible classes in this way, we can easily test the safety of publishing the social network data.
This work was partially supported by NSC (Taiwan) Grant 98-2221-E-001-013-MY3.
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References
Baader, F., Nutt, W.: Basic description logics. In: Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.) Description Logic Handbook, pp. 47–100. Cambridge University Press (2002)
Blackburn, P., de Rijke, M., Venema, Y.: Modal Logic. Cambridge University Press (2001)
Boyd, J.P., Everett, M.G.: Relations, residuals, regular interiors, and relative regular equivalence. Social Networks 21(2), 147–165 (1999)
Cardon, A., Crochemore, M.: Partitioning a graph in o(|a|log2|v|). Theoretical Computer Science 19, 85–98 (1982)
Chiang, Y.C., Hsu, T.-S., Kuo, S., Liau, C.J., Wang, D.W.: Preserving confidentiality when sharing medical database with the Cellsecu system. International Journal of Medical Informatics 71, 17–23 (2003)
de Rijke, M.: Description logics and modal logics. In: Proceedings of the 1998 International Workshop on Description Logics, DL 1998 (1998)
Hanneman, R.A., Riddle, M.: Introduction to Social Network Methods. University of California, Riverside (2005)
Hsu, T.-S., Liau, C.-J., Wang, D.-W.: A logical model for privacy protection. In: Davida, G.I., Frankel, Y. (eds.) ISC 2001. LNCS, vol. 2200, pp. 110–124. Springer, Heidelberg (2001)
Lerner, J.: Role assignments. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 216–252. Springer, Heidelberg (2005)
Liau, C.J.: Social networks and granular computing. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 8333–8345. Springer (2009)
Lorrain, F., White, H.C.: Structural equivalence of individuals in social networks. Journal of Mathematical Sociology 1, 49–80 (1971)
Marx, M., Masuch, M.: Regular equivalence and dynamic logic. Social Networks 25(1), 51–65 (2003)
Nardi, D., Brachman, R.J.: An introduction to description logics. In: Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.) Description Logic Handbook, pp. 5–44. Cambridge University Press (2002)
Paige, R., Tarjan, R.E.: Three partition refinement algorithms. SIAM Journal on Computing 16(6), 973–989 (1987)
Pawlak, Z.: Rough Sets–Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers (1991)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)
Schild, K.: A correspondence theory for terminological logics: Preliminary report. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence, pp. 466–471 (1991)
Schmidt-Schauß, M., Smolka, G.: Attributive concept descriptions with complements. Artificial Intelligence 48(1), 1–26 (1991)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 571–588 (2002)
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)
Wang, D.W., Liau, C.J., Hsu, T.-S.: Medical privacy protection based on granular computing. Artificial Intelligence in Medicine 32(2), 137–149 (2004)
Wang, D.W., Liau, C.J., Hsu, T.-S.: An epistemic framework for privacy protection in database linking. Data and Knowledge Engineering 61(1), 176–205 (2007)
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Hsu, Ts., Liau, CJ., Wang, DW. (2013). Privacy-Preserving Social Network Publication Based on Positional Indiscernibility. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds) Scalable Uncertainty Management. SUM 2013. Lecture Notes in Computer Science(), vol 8078. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40381-1_24
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DOI: https://doi.org/10.1007/978-3-642-40381-1_24
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