K-Anonymity for Privacy Preserving Crime Data Publishing in Resource Constrained Environments

Abstract

Mobile crime report services have become a pervasive approach to enabling community-based crime reporting (CBCR) in developing nations. These services hold the advantage of facilitating law enforcement when resource constraints make using standard crime investigation approaches challenging. However, CBCRs have failed to achieve widespread popularity in developing nations because of concerns for privacy. Users are hesitant to make crime reports with out strong guarantees of privacy preservation. Furthermore, oftentimes lack of data mining expertise within the law enforcement agencies implies that the reported data needs to be processed manually which is a time-consuming process. In this paper we make two contributions to facilitate effective and efficient CBCR and crime data mining as well as to address the user privacy concern. The first is a practical framework for mobile CBCR and the second, is a hybrid k-anonymity algorithm to guarantee privacy preservation of the reported crime data. We use a hierarchy-based generalization algorithm to classify the data to minimize information loss by optimizing the nodal degree of the classification tree. Results from our proof-of-concept implementation demonstrate that in addition to guaranteeing privacy, our proposed scheme offers a classification accuracy of about 38% and a drop in information loss of nearly 50% over previous schemes when compared on various sizes of datasets. Performance-wise we observe an average improvement of about 50ms proportionate to the size of the dataset.