Skip to main content
SpringerLink
Log in

Ensuring Security and Privacy Preservation for the Publication of Rating Datasets

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Recommender systems are proposed to recommend the suitable artifact(s) to the target user. They are applied in several real-life systems such as Google, Facebook, Twitter, eBay, Amazon, PlayStore’s Android, and AppStore’s Apple. Generally, they are based on ratting datasets. Aside from recommender systems, the ratting datasets can also be shared with the data analyst. However, they have serious issues that must be considered when they are utilized, e.g., privacy violation issues. To address privacy violation issues in rating datasets, a privacy preservation model, (\(l^{p_1}, \dots ,l^{p_n}\))-Privacy, is proposed. Although this privacy preservation model can address privacy violation issues in ratting datasets, it is highly complex and less effective. To rid these vulnerabilities of (\(l^{p_1}, \dots ,l^{p_n}\))-Privacy, a new privacy preservation model for rating datasets is proposed in this work. With the proposed model, aside from privacy preservation issues, the complexity and the data utility are maintained as much as possible. Furthermore, the proposed model’s effectiveness and efficiency are evaluated by extensive experiments. From the experimental results, they show that the proposed model is an effective and efficient privacy preservation model for ratting datasets.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Algorithm 1
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

The data that support the findings of this study are openly available in “Maxwell Harper F, Konstan JA. The movielens datasets: history and context. ACM Trans Interact Intell Syst. 2015;5(4):191–1919.” at “https://grouplens.org/datasets/movielens/”, reference number [32].

References

  1. Bobadilla J, Ortega F, Hernando A, Gutiérrez A. Recommender systems survey. Knowl-Based Syst. 2013;46:109–32.

    Article  Google Scholar 

  2. Jie L, Dianshuang W, Mao M, Wang W, Zhang G. Recommender system application developments: a survey. Decis Support Syst. 2015;74:12–32.

    Article  Google Scholar 

  3. Zhang S, Yao L, Sun A, Tay Y. Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv. 2019;52(1):5:1-5:38.

    Google Scholar 

  4. Ramakrishnan N, Keller BJ, Mirza BJ, Grama AY, Karypis G. Privacy risks in recommender systems. IEEE Internet Comput. 2001;5(6):54–62.

    Article  Google Scholar 

  5. Riyana S. (lp1,..., lpn)-privacy: privacy preservation models for numerical quasi-identifiers and multiple sensitive attributes. J Ambient Intell Humaniz Comput. 2021;12:9713–29. https://doi.org/10.1007/s12652-020-02715-3.

    Article  Google Scholar 

  6. Riyana S, Nanthachumphu S, Riyana N. Achieving privacy preservation constraints in missing-value datasets. SN Comput Sci. 2020;1

  7. Riyana S, Sasujit K, Homdoung N, Chaichana T, Punsaensri T. Effective privacy preservation models for rating datasets. ECTI Trans Compu Inform Technol (ECTI-CIT). 2022;17(1):1–13.

    Google Scholar 

  8. Riyana S, Natwichai J. Privacy preservation for recommendation databases. SOCA. 2018;12(3):259–73.

    Article  Google Scholar 

  9. The users’ perspective on the privacy-utility trade-offs in health recommender systems. International Journal of Human-Computer Studies. 2019;121:108–121. Advances in Computer-Human Interaction for Recommender Systems.

  10. Al-Rubaie M, Chang JM. Privacy-preserving machine learning: threats and solutions. IEEE Secur Priv. 2019;17(2):49–58.

    Article  Google Scholar 

  11. Polatidis N, Georgiadis CK, Pimenidis E, Mouratidis H. Privacy-preserving collaborative recommendations based on random perturbations. Expert Syst Appl. 2017;71:18–25.

    Article  Google Scholar 

  12. Riyana N, Riyana S, Nanthachumphu S, Sittisung S, Duangban D. Privacy violation issues in re-publication of modification datasets. In: Intelligent computing and optimization. Cham: Springer International Publishing; 2021, p. 938–953.

  13. Riyana S, Harnsamut N, Sadjapong U, Nanthachumphu S, Riyana N. Privacy preservation for continuous decremental data publishing. In: Image processing and capsule networks. Cham: Springer International Publishing; 2021, p. 233–243.

  14. Riyana S, Riyana N, Nanthachumphu S. An effective and efficient heuristic privacy preservation algorithm for decremental anonymization datasets. In: Image processing and capsule networks. Cham: Springer International Publishing; 2021, p. 244–257.

  15. Wieringa J, Kannan PK, Ma X, Reutterer T, Risselada H, Skiera B. Data analytics in a privacy-concerned world. J Bus Res. 2021;122:915–25.

    Article  Google Scholar 

  16. Zheng X, Cai Z, Yu J, Wang C, Li Y. Follow but no track: privacy preserved profile publishing in cyber-physical social systems. IEEE Internet Things J. 2017;4(6):1868–78.

    Article  Google Scholar 

  17. Kara BC, Eyupoglu C. Anonymization methods for privacy-preserving data publishing. In: Smart applications with advanced machine learning and human-centred problem design. Cham: Springer International Publishing; 2023, p. 145–159.

  18. Kulkarni YR, Jagdale B, Sugave SR. Optimized key generation-based privacy preserving data mining model for secure data publishing. Adv Eng Softw. 2023;175:103332.

    Article  Google Scholar 

  19. Srijayanthi S, Sethukarasi T. Design of privacy preserving model based on clustering involved anonymization along with feature selection. Comput Secur. 2023;126:12.

    Article  Google Scholar 

  20. Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertain Fuzziness Knowl-Based Syst. 2002;10(5):571–88.

    Article  MathSciNet  Google Scholar 

  21. Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M. L-diversity: privacy beyond k-anonymity. In: 22nd International Conference on Data Engineering (ICDE’06); April 2006. p. 24–24.

  22. Riyana S, Harnsamut N, Soontornphand T, Natwichai J. (k, e)-anonymous for ordinal data. In: 2015 18th International Conference on network-based information systems; Sep. 2015. p. 489–493.

  23. Riyana S, Riyana N, Nanthachumphu S. Enhanced (k,e)-anonymous for categorical data. In: Proceedings of the 6th International Conference on software and computer applications, ICSCA ’17, New York, NY, USA; 2017. p. 62–67. ACM.

  24. Zhang Q, Koudas N, Srivastava D, Yu T. Aggregate query answering on anonymized tables. In: 2007 IEEE 23rd International Conference on data engineering; April 2007, p. 116–125.

  25. Li N, Li T, Venkatasubramanian S. t-closeness: Privacy beyond k-anonymity and l-diversity. In: 2007 IEEE 23rd International Conference on data engineering; April 2007, p. 106–115.

  26. Fung Benjamin CM, Cao M, Desai Bipin C, Xu H. Privacy protection for rfid data. In: Proceedings of the 2009 ACM Symposium on applied computing, SAC ’09, New York, NY, USA; 2009. p. 1528–1535. ACM.

  27. Xiao X, Tao Y. Anatomy: simple and effective privacy preservation; 01 2006. p. 139–150.

  28. Abdalaal A, Nergiz ME, Saygin Y. Privacy-preserving publishing of opinion polls. Comput Secur. 2013;37:143–54.

    Article  Google Scholar 

  29. Gal T, Chen Z, Gangopadhyay A. A privacy protection model for patient data with multiple sensitive attributes. IJISP. 2008;2:28–44.

    Google Scholar 

  30. Susan S, Christopher T. Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes. Springerplus. 2016;5:964.

    Article  Google Scholar 

  31. Nergiz ME, Clifton C. Thoughts on k-anonymization. In: 22nd International Conference on Data Engineering Workshops (ICDEW’06); April 2006. p. 96.

  32. Maxwell Harper F, Konstan JA. The movielens datasets: history and context. ACM Trans Interact Intell Syst. 2015;5(4):191–1919.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Maejo University (MJU), Chiangmai-Phrao Road, Maejo, Sansai, Chiang Mai, 50290, Thailand

    Surapon Riyana & Noppamas Riyana

Authors
  1. Surapon Riyana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noppamas Riyana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Surapon Riyana.

Ethics declarations

Conflict of Interest

Author declares that they have no Conflict of interest.

Ethical Approval

This paper does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advanced Computing: Innovations and Applications” guest edited by Sanjay Madria, Parteek Bhatia, Priyanka Sharma and Deepak Garg.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Riyana, S., Riyana, N. Ensuring Security and Privacy Preservation for the Publication of Rating Datasets. SN COMPUT. SCI. 5, 340 (2024). https://doi.org/10.1007/s42979-024-02690-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-02690-y

Keywords

Search

Navigation