A data privacy preservation approach and a case study in data analytics

Data Privacy (DP) has always been an issue and more so today than ever before because of the advanced tools available to take advantage of data for all sorts of reasons including unethical. It has, therefore, become one of the big challenges that Big Data has thrown about in recent years. There are a number of attempts at dealing with DP using mainly data encoding, homomorphic encryption in particular, and other mathematical devices that allow sets to be worked on in place of others for the benefit of getting the same or equivalent solutions. They do, however, have limitations often due to the high dimensionality and extremely large volume of the data. The curse of dimensionality and volume are of course inherent to the concept of Big Data. In this paper, we suggest a new approach that relies on complexity theory and NP-Completeness in particular. We describe our approach and illustrate it on a very common problem in data analytics, namely clustering or unsupervised machine learning. Results and their discussion will be included.