Comparative Analysis of Privacy Preserving Approaches for Collaborative Data Processing

  • Urvashi Solanki
  • Bintu KadhiwalaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)


Data collection by public and private organizations is increasing for extracting hidden knowledge from it that may be used for assisting decision making process. Moreover, availability of high speed internet and sophisticated data mining tools make sharing of this collected data across various organizations possible. As a consequence, these organizations may share and combine their datasets to retrieve the improved result from the combined data using collaborative data processing. Sharing of such data as it is between collaborative organizations may compromise individual’s privacy as the collected data may contain sensitive information about individuals in its original form. To address this challenge, two main categories of privacy preserving approaches viz. the non-cryptography based approach and the cryptography based approach can be utilized. This paper aims to discuss an insight of these approaches and to highlight the parametric comparison.


Collaborative data processing Privacy preserving Cryptographic Non-cryptographic 


  1. 1.
    Chen, B.C., Kifer, D., Lefevre, K., Machanavajjhala, A.: Privacy-preserving data publishing. Found. Trends Databases 2(1–2), 1–167 (2009)CrossRefGoogle Scholar
  2. 2.
    Ciriani, V., Di Vimercati, S.D.C., Foresti, S., Samarati, P.: k-anonymous data mining: a survey. In: Aggarwal, C.C., Yu, P.S. (eds.) Privacy-Preserving Data Mining: Models and Algorithms, pp. 105–136. Springer, Boston (2008)CrossRefGoogle Scholar
  3. 3.
    The Economist, The end of privacy, p. 15 (1999).
  4. 4.
    Zhan, Z.: Privacy-Preserving Collaborative Data Mining. Ph.D thesis, University of Ottawa, Canada (2006)Google Scholar
  5. 5.
    Abbas, A., Khan, S.: A review on the state-of-the-art privacy-preserving approaches in e-health clouds. J. Biomed. Health Inf. IEEE 18(4), 1431–1441 (2014)CrossRefGoogle Scholar
  6. 6.
    Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Lenz, R., Longhurst, J., Nordholt, E.S., Seri, G., Wolf, P.: Handbook on Statistical Disclosure Control, ESSnet on Statistical Disclosure Control, version 1.0 (2006)Google Scholar
  7. 7.
    Pedersen, T.B., Saygin, Y. and Savas, E.: Secret Sharing vs. Encryption-Based Techniques for Privacy Preserving Data Mining (2007)Google Scholar
  8. 8.
    Das, K.: Privacy preserving distributed data mining based on multi-objective optimization and algorithmic game theory. Ph.D thesis, University of Maryland, Baltimore County (2009)Google Scholar
  9. 9.
    Fung, B., Wang, K., Chen, R., Yu, P.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. ACM 42(4), 1–53 (2010). Article 14CrossRefGoogle Scholar
  10. 10.
    Aldeen, Y.A.A.S., Salleh, M., Razzaque, M.A.: A comprehensive review on privacy preserving data mining. SpringerPlus 4(1), 694 (2015)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Luo, Y., Zhao, Y., Le, J.: A survey on privacy preserving data mining. In: First International Workshop on Database Technology and Applications, pp. 111–114. IEEE (2009)Google Scholar
  12. 12.
    Dalenius, T.: Finding a needle in a haystack - or identifying anonymous census record. J. Official Stat. 2(3), 329–336 (1986)Google Scholar
  13. 13.
    Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)CrossRefGoogle Scholar
  14. 14.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006), p. 24 (2006)Google Scholar
  15. 15.
    Aggarwal, C.C., Yu, P.S.: On static and dynamic methods for condensation-based privacy-preserving data mining. In: ACM Transactions on Database Systems (TODS), vol. 33, no. 1 (2008)CrossRefGoogle Scholar
  16. 16.
    Chen, K., Liu, L.: Privacy preserving data classification with rotation perturbation. In: Fifth IEEE International Conference on Data Mining (ICDM 2005) (2005)Google Scholar
  17. 17.
    Mivule, K.: Utilizing noise addition for data privacy, an overview. In: International Conference on Information and Knowledge Engineering, Las Vegas, USA, pp. 65–71 (2012)Google Scholar
  18. 18.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 19th ACM SIGMOD Conference on Management of Data, vol. 29, no. 2, pp. 439–450. ACM (2000)Google Scholar
  19. 19.
    Aggarwal, C.C., Philip, S.Y.: A survey of randomization methods for privacy-preserving data mining. In: Aggarwal, C.C., Yu, P.S. (eds.) privacy-preserving data mining. Advances in Database Systems, vol. 34, pp. 137–156. Springer, Boston (2008)CrossRefGoogle Scholar
  20. 20.
    Huang, Z., Du, W., Chen, B.: Deriving private information from randomized data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 37–48 (2005)Google Scholar
  21. 21.
    Xu, L., Jiang, C., Wang, J., Yuan, J., Ren, Y.: Information security in big data: privacy and data mining. IEEE Access 2, 1149–1176 (2014)CrossRefGoogle Scholar
  22. 22.
    Yao, A.C.C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, pp. 162–167. IEEE (1986)Google Scholar
  23. 23.
    Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. ACM SIGKDD Explor. Newsl. 4(2), 28–34 (2002)CrossRefGoogle Scholar
  24. 24.
    Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Priv. Confidentiality 1(1), 59–98 (2009)Google Scholar
  25. 25.
    Ge, X., Yan, L., Zhu, J., Shi, W.: Privacy-preserving distributed association rule mining based on the secret sharing technique. In: 2nd International Conference on Software Engineering and Data Mining (SEDM), pp. 345–350. IEEE (2010)Google Scholar
  26. 26.
    Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Blakley, G.R.: Safeguarding cryptographic keys. In: Proceedings of the National Computer Conference, vol. 48, pp. 313–317 (1979)Google Scholar
  28. 28.
    Zhan, J., Blosser, G., Yang, C., Singh, L.: Privacy-preserving collaborative social networks. In: International Conference on Intelligence and Security Informatics, pp. 114–125. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  29. 29.
    Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. Found. Secure Comput. 4(11), 169–180 (1978)MathSciNetGoogle Scholar
  30. 30.
    Yang, Z., Zhong, S., Wright, R.N.: Privacy preserving classification of customer data without loss of accuracy. In: Proceedings of the 5th SIAM International Conference on Data Mining, pp. 92–102 (2005)Google Scholar
  31. 31.
    Yi, X., Paulet, R., Bertino, E.: Homomorphic Encryption and Applications, vol. 3 (2014)zbMATHGoogle Scholar
  32. 32.
    Burnett, L., Barlow-Stewart, K., Proos, A.L., Aizenberg, H.: The “GeneTrustee”: a universal identification system that ensures privacy and confidentiality for human genetic databases. J. Law Med. 10(4), 506–513 (2003)Google Scholar
  33. 33.
    Cox, L.H.: Suppression methodology and statistical disclosure control. J. Am. Stat. Assoc. 75(370), 377–385 (1980)CrossRefGoogle Scholar
  34. 34.
    Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(5), 571–588 (2002)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information (Abstract). In: Proceedings of the Seventeenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 98), vol. 98, p. 188 (1998)Google Scholar
  36. 36.
    Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and specialization, Technical report SRI-CSL-98-04, SRI Intl., pp. 101–132 (1998)Google Scholar
  37. 37.
    Domingo-Ferrer, J., Torra, V.: A critique of k-anonymity and some of its enhancements. In: Third International Conference on Availability, Reliability and Security, pp. 990–993. IEEE (2008)Google Scholar
  38. 38.
    Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: 23rd International Conference on Data Engineering (ICDE 2007), pp. 106–115. IEEE (2007)Google Scholar
  39. 39.
    Yin, Y., Kaku, I., Tang, J., Zhu, J.: Privacy-preserving data mining. Data Mining Concepts, Methods and Applications in Management and Engineering Design, pp. 101–119. Springer, London (2011)Google Scholar
  40. 40.
    Aggarwal, C.C., Yu, P.S.: A condensation approach to privacy preserving data mining. In: Proceedings of the International Conference on Extending Database Technology (EDBT), pp. 183–199 Springer, Heidelberg (2004)CrossRefGoogle Scholar
  41. 41.
    Li, D., He, X., Cao, L., Chen, H.: Permutation anonymization. J. Intell. Inf. Syst. 47(3), 427–445 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Engineering DepartmentSarvajanik College of Engineering and TechnologySuratIndia

Personalised recommendations