Approximate DBSCAN on obfuscated data

Abstract

With the emergence of remote storage, computation facilities, and the availability of high-speed data connectivity — cloud computation has become the call for the day. In this scenario, security and computability of data have emerged as two crucial aspects which often conflict with each other. An efficient solution is to build a trade-off between the two. In this work, we propose a novel Hilbert-Curve-based data encoding scheme (HC encoding) which obfuscates the data points. We use the obfuscated data in DBSCAN clustering and obtain an approximated output with respect to the original clustering (without invoking the plaintext data at any stages of the computation). HC encoding has a provision to modulate the degree of obfuscation. The correctness of the clustering performance decreases with the increase in obfuscation and vice versa. The empirical study is carried out on 8 regular datasets and a large, color image dataset, CIFAR10. Two key applications are explored– (i) clustering of homogeneous images and (ii) clustering of heterogeneous images originating from two different sources. The empirical results show the effectiveness of the HC encoding to provide approximate clustering performances on the obfuscated multimedia data as well as regular datasets. The outcomes also manifest HC encoding’s superiority against the comparing methods which operate under a similar fully private constraint.