Optimal Trust Mining and Computing on Keyed MapReduce

  • Huafei Zhu
  • Hong Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7159)


This paper studies trust mining in the framework of keyed MapReduce and trust computing in the context of the Bayesian inferences and makes the following two-fold contributions:

In the first fold, a general method for trust mining is introduced and formalized in the context of keyed MapReduce functions. A keyed MapReduce function is a classic MapReduce function associated with a common reference keyword set so that a document is projected on the specified common reference set rather the whole dictionary as that defined in the classic MapReduce function. As a result, keyed MapReduce functions allow one to define flexible trust mining procedures: a look-up table which records the comments of neighbors can be constructed from the inverted index of the keyed MapReduce function;

In the second fold, a new method for trust computing is introduced and formalized in the context of maximum likelihood distribution. A look-up table generated in the trust mining stage is now viewed as the current state of the target server and then the maximum likelihood distribution over the look-up table is deduced. We show that the proposed trust computing mechanism is optimal (an upper bound of trust values).


Bayesian inference Maximum likelihood distribution keyed-MapReduce trust computing trust mining 


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  1. 1.
    Salehi-Abari, A., White, T.: Witness-Based Collusion and Trust-Aware Societies. CSE (4), 1008–1014 (2009)Google Scholar
  2. 2.
    Salehi-Abari, A., White, T.: The relationship of trust, demand, and utility: Be more trustworthy, then i will buy more. In: PST 2010, pp. 72–79 (2010)Google Scholar
  3. 3.
    Salehi-Abari, A., White, T.: Trust Models and Con-Man Agents: From Mathematical to Empirical Analysis. In: AAAI 2010, pp. 842–847 (2010)Google Scholar
  4. 4.
    Beth, T., Borcherding, M., Klein, B.: Valuation of Trust in Open Networks. In: Gollmann, D. (ed.) ESORICS 1994. LNCS, vol. 875, pp. 3–18. Springer, Heidelberg (1994)Google Scholar
  5. 5.
    Cramer, R., Shoup, V.: A Practical Public Key Cryptosystem Provably Secure Against Adaptive Chosen Ciphertext Attack. In: Krawczyk, H. (ed.) CRYPTO 1998. LNCS, vol. 1462, pp. 13–25. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: OSDI 2004, pp. 137–150 (2004)Google Scholar
  7. 7.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  8. 8.
    Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. CACM 53(1), 72–77 (2010)CrossRefGoogle Scholar
  9. 9.
    Golbeck, J.: Computing and applying trust in web-based social networks, Ph.d dissertation, University of Maryland, College Park (2005)Google Scholar
  10. 10.
    Gudes, E., Gal-Oz, N., Grubshtein, A.: Methods for Computing Trust and Reputation While Preserving Privacy. In: DBSec 2009, pp. 291–298 (2009)Google Scholar
  11. 11.
    Gal-Oz, N., Yahalom, R., Gudes, E.: Identifying Knots of Trust in Virtual Communities. In: Wakeman, I., Gudes, E., Jensen, C.D., Crampton, J. (eds.) Trust Management V. IFIP AICT, vol. 358, pp. 67–81. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Shah, M.A., Baker, M., Mogul, J.C., Swaminathan, R.: Auditing to keep online storage services honest. In: Proceedings of the 11th USENIX Workshop on Hot topics in Operating Systems, HOTOS 2007. USENIX Association, Berkeley (2007)Google Scholar
  13. 13.
    Sabater, J., Sierra, C.: REGRET: reputation in gregarious societies. In: Agents 2001, pp. 194–195 (2001)Google Scholar
  14. 14.
    Vaudenay, S.: On Privacy Models for RFID. In: Kurosawa, K. (ed.) ASIACRYPT 2007. LNCS, vol. 4833, pp. 68–87. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Yahalom, R., Klein, B., Beth, T.: Trust-Based Navigation in Distribution Systems. Computing Systems 7(1), 45–73 (1994)Google Scholar
  16. 16.
    Yu, B., Singh, M.P.: A Social Mechanism of Reputation Management in Electronic Communities. In: CIA 2000, pp. 154–165 (2000)Google Scholar
  17. 17.
    Zhu, H., Bao, F., Liu, J.: Computing of Trust in ad-hoc networks. In: Leitold, H., Markatos, E.P. (eds.) CMS 2006. LNCS, vol. 4237, pp. 1–11. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Zhu, H., Bao, F.: Quantifying Trust Metrics of Recommendation Systems in Ad-Hoc Networks. In: Wireless Communications and Networking Conference (WCNC 2007). IEEE (March 2007)Google Scholar
  19. 19.
    Zhu, H., Bao, F.: Computing of Trust in Complex Environments. In: 18th IEEE Annual International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 2007), Greece, September 3-7 (2007)Google Scholar
  20. 20.
    Zhu, H., Bao, F.: Private Searching on MapReduce. In: Katsikas, S., Lopez, J., Soriano, M. (eds.) TrustBus 2010. LNCS, vol. 6264, pp. 93–101. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huafei Zhu
    • 1
  • Hong Xiao
    • 2
  1. 1.I2R, A*STARSingapore
  2. 2.TEI, AFEU, Xi’AnChina

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