Skip to main content
SpringerLink
Log in

Privacy Preserving Data Mining: A Review of the State of the Art

  • Conference paper
  • First Online:
Harmony Search and Nature Inspired Optimization Algorithms

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 741))

Abstract

Safeguarding of security in information mining has risen as an outright essential for trading secret data as far as information investigation, approval, and distributing. Constantly raising web phishing postured serious danger on across the board proliferation of delicate data over the web. Then again, the questionable sentiments and conflicts intervened unwillingness of different data suppliers towards the unwavering quality insurance of information from exposure frequently comes about absolute dismissal in information sharing or off base data sharing. This article gives an all-encompassing outline on new point of view and precise translation of a rundown distributed literary works through their fastidious association in subcategories. The crucial ideas of the current protection safeguarding information mining strategies, their benefits, and deficiencies are displayed. The present security protecting information mining methods are ordered in light of contortion, affiliation administer, shroud affiliation control, scientific categorization, bunching, cooperative characterization, outsourced information mining, disseminated, and k-anonymity, where their remarkable points of interest and hindrances are underlined. This watchful investigation uncovers the past improvement, show examine challenges, future patterns, the holes and weaknesses. Promote huge improvements for more powerful security insurance and safeguarding are confirmed to be compulsory.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aggarwal, C.C., Yu, P.S.: A general survey of privacy-preserving data mining models and algorithms. In: Privacy Preserving Data Mining (Chap. 2), pp. 11–52. Springer, New York (2008)

    Google Scholar 

  2. Arunadevi, M., Anuradha, R.: Privacy preserving outsourcing for frequent item set mining. Int. J. Innov. Res. Comp. Commun. Eng. 2(1), 3867–3873 (2014)

    Google Scholar 

  3. Baotou, T.: Research on privacy preserving classification data mining based on random perturbation. In: X. Zhang, H. Bi pp. 1–6 (2010)

    Google Scholar 

  4. Belwal, R., Varshney, J., Khan, S.: Hiding sensitive association rules efficiently by introducing new variable hiding counter. In: IEEE International Conference on Service Operations and Logistics, and Informatics, vol. 1, pp. 130–134, IEEE/SOLI 2008 (2013)

    Google Scholar 

  5. Chan, J., Keng, J.: Privacy protection in outsourced association rule mining using distributed servers and its privacy notions, pp. 1–5 (2013)

    Google Scholar 

  6. Ciriani, V., Vimercati, S.D.C., Foresti, S., Samarati, P.: k-anonymous data mining: a survey. In: Privacy-Preserving Data Mining, pp. 105–136. Springer, New York (2008)

    Google Scholar 

  7. Dehkordi, M.N.M., Badie, K., Zadeh, A.K.A.: A novel method for privacy preserving in association rule mining based on genetic algorithms. J. Softw. 4(6), 555–562 (2009)

    Article  Google Scholar 

  8. Deivanai, P., Nayahi, J., Kavitha, V.: A hybrid data anonymization integrated with suppression for preserving privacy in mining multi party data. In: IEEE International Conference on Recent Trends in Information Technology (ICRTIT) (2011)

    Google Scholar 

  9. Dev, H., Sen, T., Basak, M., Ali, M.E.: An approach to protect the privacy of cloud data from data mining based attacks. In: IEEE 2012 SC Companion High Performance Computing, Networking, Storage and Analysis (SCC) (2012)

    Google Scholar 

  10. Ding, X., Yu, Q., Li, J., Liu, J., Jin, H.: Distributed anonymization for multiple data providers in a cloud system. In: Database Systems for Advanced Applications. Springer, Berlin (2013)

    Google Scholar 

  11. Giannotti, F., Lakshmanan, L.V.S., Monreale, A., Pedreschi, D., Wang, H.: Privacy-preserving mining of association rules from outsourced transaction databases. IEEE Syst. J. 7(3), 385–395 (2013)

    Article  Google Scholar 

  12. Gkoulalas-Divanis, A., Verykios, V.S.: Exact knowledge hiding through database extension. IEEE Trans. Knowl. Data Eng. 21(5), 699–713 (2009)

    Article  Google Scholar 

  13. Harnsamut, N., Natwichai, J.: A novel heuristic algorithm for privacy preserving of associative classification. In: PRICAI 2008: Trends in Artificial Intelligence, pp. 273–283. Springer, Berlin (2008)

    Google Scholar 

  14. Inan, A., Saygin, Y.: Privacy preserving spatio-temporal clustering on horizontally partitioned data. In: Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6202 LNAI, pp. 187–198 (2010)

    Google Scholar 

  15. Islam, M.Z., Brankovic, L.: Privacy preserving data mining: a noise addition framework using a novel clustering technique. Knowl Based Syst. 24(8), 1214–1223 (2011)

    Article  Google Scholar 

  16. Jain, Y.Y.K., Yadav, V.K.V.V.K., Panday, G.G.S.: An efficient association rule hiding algorithm for privacy preserving data mining. Int. J. Comp. Sci Eng. 3(7), 2792–2798 (2011)

    Google Scholar 

  17. Kamakshi, P.: Automatic detection of sensitive attribute in PPDM. In: IEEE International Conference on Computational Intelligence & Computing Research (ICCIC) (2012)

    Google Scholar 

  18. Kamakshi, P., Babu, A.V.: Preserving privacy and sharing the data in distributed environment using cryptographic technique on perturbed data 2(4) (2010)

    Google Scholar 

  19. Karim, R., Rashid, M., Jeong, B., Choi, H.: Transactional databases, pp. 303–319 (2012)

    Google Scholar 

  20. Kasthuri, S., Meyyappan, T.: Detection of sensitive items in market basket database using association rule mining for privacy preserving. In: IEEE International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) (2013)

    Google Scholar 

  21. Kerschbaum, F., Julien, V.: Privacy-preserving data analytics as an outsourced service. In: Proceedings of the 2008 ACM Workshop on Secure Web Services. ACM (2008)

    Google Scholar 

  22. Keshavamurthy, B.N., Khan, A.M., Toshniwal, D.: Privacy preserving association rule mining over distributed databases using genetic algorithm. Neural Comput. Appl. pp. 351–364 (2013)

    Google Scholar 

  23. Kumbhar, M.N., Kharat, R.: Privacy preserving mining of association rules on horizontally and vertically partitioned data: a review paper. In: 12th IEEE International Conference on Hybrid Intelligent Systems (HIS), pp. 231–235 (2012)

    Google Scholar 

  24. Lai, J., Li, Y., Deng, R.H., Weng, J., Guan, C., Yan, Q.: Towards semantically secure outsourcing of association rule mining on categorical data. Inf. Sci. (NY) 267, 267–286 (2014)

    Article  MathSciNet  Google Scholar 

  25. Li, Y.: Privacy-Preserving and Reputation System in Distributed Computing with Untrusted Parties, no. July. Pro- Quest LLC (2013). Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved (2013)

    Google Scholar 

  26. Li, W., Liu, J.: Privacy preserving association rules mining based on data disturbance and inquiry limitation. In: 2009 Fourth International Conference on Internet Computer Science Engineering, pp. 24–29 (2009)

    Google Scholar 

  27. Lin, K.W., Lo, Y.-C.: Efficient algorithms for frequent pattern mining in many-task computing environments. Knowl Based Syst. 49, 10–21 (2013)

    Article  Google Scholar 

  28. Loukides, G., Gkoulalas-divanis, A.: Expert systems with applications utility-preserving transaction data anonymization with low information loss. Expert Syst. Appl. 39(10), 9764–9777 (2012)

    Article  Google Scholar 

  29. Loukides, G., Gkoulalas-Divanis, A., Shao, J.: Efficient and flexible anonymization of transaction data. Knowl. Inf. Syst. 36(1), 153–210 (2012)

    Article  Google Scholar 

  30. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1), 3 (2007)

    Article  Google Scholar 

  31. Malina, L., Hajny, J.: Efficient security solution for privacy-preserving cloud services. In: 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 23–27 (2013)

    Google Scholar 

  32. Matwin, S.: Privacy-preserving data mining techniques: survey and challenges. In: Discrimination & Privacy in the Information Society, pp. 209–221. Springer, Berlin (2013)

    Google Scholar 

  33. Monreale, A., Pedreschi, D., Pensa, R.G., Pinelli, F.: Anonymity preserving sequential pattern mining. Artif. Intell. Law 22(2), 141–173 (2014)

    Article  Google Scholar 

  34. Mukkamala, R., Ashok, V.G.: Fuzzy-based methods for privacy-preserving data mining. In: IEEE Eighth International Conference on Information Technology: New Generations (ITNG) (2011)

    Google Scholar 

  35. Naeem, M., Asghar, S., Fong, S.: Hiding sensitive association rules using central tendency. In: 6th International Conference on Advanced Information Management and Service (IMS), pp. 478–484 (2010)

    Google Scholar 

  36. Nanavati, N., Jinwala, D.: Privacy preservation for global cyclic associations in distributed databases. Procedia Technol. 6, 962–969 (2012)

    Article  Google Scholar 

  37. Nayak, G., Devi, S.: A survey on privacy preserving data mining: approaches and techniques. Int. J. Eng. Sci. Tech. 3(3), 2117–2133 (2011)

    Google Scholar 

  38. Nergiz, M.E., Gök, M.Z.: Hybrid k-anonymity. Comput. Secur. 44, 51–63 (2014)

    Article  Google Scholar 

  39. Nergiz, M.E., Christopher, C., Ahmet, E.N.: Multirelational k-anonymity. IEEE Trans. Knowl. Data Eng. 21(8), 1104–1117 (2009)

    Article  Google Scholar 

  40. Nergiz, M.E., Gök, M.Z., Özkanlı, U.: Preservation of utility through hybrid k-anonymization. In: Trust, Privacy, and Security in Digital Business, pp. 97–111. Springer, Berlin (2013)

    Google Scholar 

  41. Nix, R., Kantarcioglu, M., Han, K.J.: Approximate privacy-preserving data mining on vertically partitioned data. In: Data and Applications Security and Privacy XXVI, pp. 129–144. Springer, Berlin (2012)

    Google Scholar 

  42. Om Kumar, C.U., Tejaswi, K., Bhargavi, P.: A distributed cloud—prevents attacks and preserves user privacy. In: 15th International Conference on Advanced Computing Technologies, ICACT (2013)

    Google Scholar 

  43. Qi, X., Zong, M.: An overview of privacy preserving data mining. Procedia Environ. Sci. 12(Icese 2011), 1341–1347 (2012)

    Google Scholar 

  44. Quoc, H., Arch-int, S., Xuan, H., Arch-int, N.: Computers in industry association rule hiding in risk management for retail supply chain collaboration. Comput. Ind. 64(7), 776–784 (2013)

    Article  Google Scholar 

  45. Raghuram, B., Gyani, J.: Privacy preserving associative classification on vertically partitioned databases. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 188–192 (2012)

    Google Scholar 

  46. Raju, R., Komalavalli, R., Kesavakumar, V.: Privacy maintenance collaborative data mining: a practical approach. In: 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET), pp. 307–311 (2009)

    Google Scholar 

  47. Sachan, A., Roy, D., Arun, P.V.: An analysis of privacy preservation techniques in data mining. In: Advances in Computing and Information Technology, vol. 3, pp. 119–128. Springer, Berlin (2013)

    Google Scholar 

  48. Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. (TKDE) 13(6), 1010–1027 (2001)

    Article  Google Scholar 

  49. Sathiyapriya, K., Sadasivam, G.S.: A survey on privacy preserving association rule mining. Int. J. Data Min Knowl. Manag. Process 3(2), 119–131 (2013)

    Article  Google Scholar 

  50. Seisungsittisunti, B., Natwichai, J.: Achieving k-anonymity for associative classification in incremental-data scenarios. In: Security-Enriched Urban Computing and Smart Grid, pp. 54–63. Springer, Berlin (2011)

    Google Scholar 

  51. Shrivastava, R., Awasthy, R., Solanki, B.: New improved algorithm for mining privacy—preserving frequent itemsets. Int. J. Comp. Sci. Inform. 1, 1–7 (2011)

    Google Scholar 

  52. Singh, M.D., Krishna, P.R., Saxena, A.: A cryptography based privacy preserving solution to mine cloud data. In: Proceedings of Third Annual ACM Bangalore Conference. ACM (2010)

    Google Scholar 

  53. Soodejani, A.T., Hadavi, M.A., Jalili, R.: k-anonymity-based horizontal fragmentation to preserve privacy in data outsourcing. In: Data and Applications Security and Privacy XXVI, pp. 263–273. Springer, Berlin (2012)

    Google Scholar 

  54. Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain Fuzziness Knowl. Based Syst. 10(5), 571–588 (2002)

    Article  MathSciNet  Google Scholar 

  55. Tai, C.-H., Huang, J.-W., Chung, M.-H.: Privacy preserving frequent pattern mining on multi-cloud environment. In: 2013 International Symposium on Biometrics and Security Technologies (ISBAST) (2013)

    Google Scholar 

  56. Tassa, T.: Secure mining of association rules in horizontally distributed databases. IEEE Trans. Knowl. Data Eng. 26(4), 970–983 (2014)

    Article  Google Scholar 

  57. Vaidya, J., Clifton, C., Kantarcioglu, M., Patterson, A.S.: Privacy-preserving decision trees over vertically partitioned data. ACM Trans. Knowl. Discov. Data 2(3), 1–27 (2008)

    Article  Google Scholar 

  58. Vatsalan, D., Christen, P., Verykios, V.S.: A taxonomy of privacy-preserving record linkage techniques. Inf. Syst. 38(6), 946–969 (2013)

    Article  Google Scholar 

  59. Vijayarani, S., Tamilarasi, A., Seethalakshmi, R.: Privacy preserving data mining based on association rule: a survey. In: IEEE International Conference on Communication and Computational Intelligence (INCOCCI) (2010a)

    Google Scholar 

  60. Vijayarani, S., Tamilarasi, A., Sampoorna, M.: Analysis of privacy preserving k-anonymity methods and techniques. In: IEEE International Conference on Communication and Computational Intelligence (INCOCCI), pp. 540–545 (2010b)

    Google Scholar 

  61. Wang, H., Hu, C., Liu, J.: Distributed mining of association rules based on privacy- preserved method. In: International Symposium on Information Science & Engineering (ISISE), pp. 494–497 (2010)

    Google Scholar 

  62. Wang, K., Yu, P.S., Chakraborty, S.: Bottom-up generalization: a data mining solution to privacy protection. In: IEEE Fourth International Conference on Data Mining (ICDM’04) (2004)

    Google Scholar 

  63. Weng, C., Chen, S., Lo, H.: A novel algorithm for completely hiding sensitive association rules. In: Eighth International Conference on Intelligent Systems Design and Applications, ISDA’08, vol. 3, pp. 202–208 (2008)

    Google Scholar 

  64. Worku, S.G., Xu, C., Zhao, J., He, X.: Secure and efficient privacy-preserving public auditing scheme for cloud storage. Comput. Electr. Eng. 40(5), 1703–1713 (2014)

    Article  Google Scholar 

  65. Xiong, L., Chitti, S., Liu, L.: k nearest neighbor classification across. In: Proceedings of the 15th ACM International Conference on Information & Knowledge Management CIKM’06, pp. 840–841 (2006)

    Google Scholar 

  66. Xu, Z., Yi, X.: Classification of privacy-preserving distributed data mining protocols. In: Sixth International Conference on Digital Information Management, pp. 337–342 (2011)

    Google Scholar 

  67. Yi, X., Zhang, Y.: Equally contributory privacy-preserving k-means clustering over vertically partitioned data. Inf. Syst. 38(1), 97–107 (2013)

    Article  Google Scholar 

  68. Ying-hua, L., Bing-ru, Y., Dan-yang, C., Nan, M.: State-of-the-art in distributed privacy preserving data mining. In: IEEE 3rd International Conference Communication Software and Networks, pp. 545–549 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chitkara University, Chandigarh, Punjab, India

    Shivani Sharma & Sachin Ahuja

Authors
  1. Shivani Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sachin Ahuja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shivani Sharma .

Editor information

Editors and Affiliations

  1. School of Engineering and Technology, BML Munjal University, Gurgaon, Haryana, India

    Neha Yadav

  2. Department of Sciences and Humanities, National Institute of Technology, Srinagar, Uttarakhand, India

    Anupam Yadav

  3. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharma, S., Ahuja, S. (2019). Privacy Preserving Data Mining: A Review of the State of the Art. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. (eds) Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_1

Download citation

Publish with us

Policies and ethics

Search

Navigation