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Privacy preservation for recommendation databases

Published: 01 December 2018 Publication History

Abstract

Since recommendation systems play an important role in the current situations where such digital transformation is highly demanded, the privacy of the individuals' collected data in the systems must be secured effectively. In this paper, the vulnerability of the existing query framework for the recommendation systems is identified. Thus, we propose to apply the well-known k-anonymity model to generalize the given recommendation databases to satisfy the privacy preservation constraint. We show that such data generalization problem which minimizes the impact on data utility is NP-hard. To tackle with such problem, an algorithm to preserve the privacy of the individuals in the recommendation databases is proposed. The idea is to avoid excessive generalizing on the databases by forming a group of similar tuples in the databases. Thus, the impact on the data utility of the generalizing such group can be minimized. Our work is evaluated by extensive experiments. From the results, it is found that our work is highly effective, i.e., the impact quantified by the data utility metrics and the errors of the query results are less than the compared algorithms, and also it is highly efficient, i.e., the execution time is less than the result of its effectiveness-comparable algorithm by more than three times.

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  • (2024)Ensuring Security and Privacy Preservation for the Publication of Rating DatasetsSN Computer Science10.1007/s42979-024-02690-y5:4Online publication date: 27-Mar-2024
  • (2022)Achieving Anonymization Constraints in High-Dimensional Data Publishing Based on Local and Global Data SuppressionsSN Computer Science10.1007/s42979-021-00936-73:1Online publication date: 1-Jan-2022
  • (2021)Privacy Preservation Techniques for Sequential Data ReleasingProceedings of the 12th International Conference on Advances in Information Technology10.1145/3468784.3470468(1-9)Online publication date: 29-Jun-2021
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Information & Contributors

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Published In

cover image Service Oriented Computing and Applications
Service Oriented Computing and Applications  Volume 12, Issue 3-4
December 2018
199 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2018

Author Tags

  1. Privacy preservation
  2. Recommendation databases
  3. k-Anonymity

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View all
  • (2024)Ensuring Security and Privacy Preservation for the Publication of Rating DatasetsSN Computer Science10.1007/s42979-024-02690-y5:4Online publication date: 27-Mar-2024
  • (2022)Achieving Anonymization Constraints in High-Dimensional Data Publishing Based on Local and Global Data SuppressionsSN Computer Science10.1007/s42979-021-00936-73:1Online publication date: 1-Jan-2022
  • (2021)Privacy Preservation Techniques for Sequential Data ReleasingProceedings of the 12th International Conference on Advances in Information Technology10.1145/3468784.3470468(1-9)Online publication date: 29-Jun-2021
  • (2021)A Privacy Preservation Model for RFID Data-Collections is Highly Secure and More Efficient than LKC-PrivacyProceedings of the 12th International Conference on Advances in Information Technology10.1145/3468784.3469853(1-11)Online publication date: 29-Jun-2021
  • (2021)An Anatomization Model for Farmer Data CollectionsSN Computer Science10.1007/s42979-021-00740-32:5Online publication date: 1-Sep-2021
  • (2020)Achieving Privacy Preservation Constraints in Missing-Value DatasetsSN Computer Science10.1007/s42979-020-00241-91:4Online publication date: 1-Jul-2020

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