The problem of preserving privacy in recommendation systems is faced in this work. The approach presented reduces the study of privacy threats to the study of frequent property set obtained from the characteristics of the objects the recommendation system provides to a target user. This study is made by defining a prominence index for each item and by using efficient methods to explore the lattice of item characteristics.


Recommendation System Privacy Prominence Index Frequent Item Sets 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining (1996)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)Google Scholar
  3. 3.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowledge-Based Systems 46, 109–132 (2013)CrossRefGoogle Scholar
  4. 4.
    Burke, R.: Knowledge-based recommender systems (2000)Google Scholar
  5. 5.
    Campan, A., Truta, T.M.: Data and structural k-anonymity in social networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PinKDD 2008. LNCS, vol. 5456, pp. 33–54. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Candillier, L., Meyer, F., Boullé, M.: Comparing state-of-the-art collaborative filtering systemsGoogle Scholar
  7. 7.
    Díaz, I., Ralescu, A.: Privacy issues in social networks: A brief survey. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012, Part IV. CCIS, vol. 300, pp. 509–518. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Díaz, I., Rodríguez-Muñiz, L.J., Troiano, L.: Fuzzy sets in data protection: strategies and cardinalities. Logic Journal of IGPL 20(4), 657–666 (2012)CrossRefGoogle Scholar
  9. 9.
    Díaz, I., Rodríguez-Mũniz, L.J., Troiano, L.: On mining sensitive rules to identify privacy threats. In: Pan, J.-S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) HAIS 2013. LNCS, vol. 8073, pp. 232–241. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. In: Mannila, H. (ed.) Data Mining and Knowledge Discovery, pp. 53–87. Kluwer, New York (2004)Google Scholar
  11. 11.
    Lee, M., Choi, P., Woo, Y.: A hybrid recommender system combining collaborative filtering with neural network. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 531–534. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Luo, X., Xia, Y., Zhu, Q.: Incremental collaborative filtering recommender based on regularized matrix factorization. Know.-Based Syst. 27, 271–280 (2012)CrossRefGoogle Scholar
  13. 13.
    McSherry, F., Mironov, I.: Differentially private recommender systems: Building privacy into the net. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 627–636. ACM, New York (2009)Google Scholar
  14. 14.
    Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5-6), 393–408 (1999)CrossRefGoogle Scholar
  15. 15.
    Salter, J., Antonopoulos, N.: Cinemascreen recommender agent: Combining collaborative and content-based filtering. IEEE Intelligent Systems 21(1), 35–41 (2006)CrossRefGoogle Scholar
  16. 16.
    Shang, S., Hui, Y., Hui, P., Cuff, P.W., Kulkarni, S.R.: Privacy preserving recommendation system based on groups. CoRR, abs/1305.0540 (2013)Google Scholar
  17. 17.
    Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)Google Scholar
  18. 18.
    Troiano, L., Díaz, I., Kriplani, A.: A recommender system based on dempster-shafer theory. In: Eurofuse, pp. 232–241 (2013)Google Scholar
  19. 19.
    Troiano, L., Díaz, I., Rodríguez-Muñiz, L.J.: A model for assessing the risk of revealing shared secrets in social networks. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012, Part IV. CCIS, vol. 300, pp. 499–508. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Troiano, L., Rodríguez-Muñiz, L.J., Ranilla, J., Díaz, I.: Interpretability of fuzzy association rules as means of discovering threats to privacy. Int. J. Comput. Math. 89(3), 325–333 (2012)CrossRefGoogle Scholar
  21. 21.
    Troiano, L., Scibelli, G.: Mining frequent itemsets in data streams within a time horizon. Data & Knowledge Engineering 89, 21–37 (2014)CrossRefGoogle Scholar
  22. 22.
    Troiano, L., Scibelli, G.: A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets. Data Mining and Knowledge Discovery 28(3), 773–807 (2014)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Troiano, L., Scibelli, G., Birtolo, C.: A fast algorithm for mining rare itemsets. In: ISDA 2009, pp. 1149–1155 (2009)Google Scholar
  24. 24.
    Zhou, B., Pei, J.: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowledge and Information Systems 28(1), 1–38 (2010)MathSciNetGoogle Scholar
  25. 25.
    Zhou, B., Pei, J., Luk, W.: A brief survey on anonymization techniques for privacy preserving publishing of social network data. SIGKDD Explor. Newsl. 10, 12–22 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luigi Troiano
    • 1
  • Irene Díaz
    • 2
  1. 1.University of SannioItaly
  2. 2.University of OviedoSpain

Personalised recommendations