A Highly Efficient and Secure Multidimensional Blocking Approach for Private Record Linkage

Privacy Preserving Record Linkage is the process of securely integrating information without compromising the privacy of the individuals described by these data. While such an effort sounds appealing for both academic and business applications, it is complicated and computationally intensive. In this paper we aspire to provide a solution to this problem by presenting a highly secure mutidimensional Privacy Preserving Blocking approach which is totally distributed and runs independently on each data holder, making it invulnerable to third party attacks. It is based on the idea of using publicly available corpora of data known as reference sets for creating k-anonymous clusters. We analytically prove that our method is secure and provide experimental results which evaluate the increased performance of our method in terms of matching accuracy and execution time.