Enhanced k-Anonymity model based on clustering to overcome Temporal attack in Privacy Preserving Data Publishing
C. N. Sowmyarani, L G Namya, G K Nidhi, Pardeep Kumar · 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · 2022
The infrastructure required for data storage and processing has become increasingly feasible, and hence, there has been a massive growth in the field of data acquisition and analysis. This acquired data is published, empowering organizations to make informed data-driven decisions based on previous trends. However, data publishing has led to the compromise of privacy as a result of the release of entity-specific information. Privacy-Preserving Data Publishing [1] can be accomplished by methods such as Data Swapping, Differential Privacy, and the likes of k-Anonymity. k-Anonymity is a well-established method used to protect the privacy of the data published. We propose a clustering-based novel algorithm named SAC or the Score, Arrange, and Cluster Algorithm to preserve privacy based on k-Anonymity. This method outperforms existing methods such as the Mondrian Algorithm by K. LeFevre and the One-pass K-means Algorithm by Jun-Lin Lin from a data quality perspective. SAC can be used to overcome temporal attack across subsequent releases of published data. To measure data quality post anonymization we present a metric that takes into account the relative loss in the information, that occurs while generalizing attribute values.