Elastic cloud platform for privacy-preserving data mining as a service

Privacy-Preserving Data Mining (PPDM) methods prevent unauthorized data disclosure during data analysis tasks executed by untrusted third parties, as in Data Mining as a Service (DMaaS) scenarios. However, PPDM models still present usability, performance, security, and practicality issues. This paper presents an elastic cloud-based model for efficient and flexible PPDM as a Service (PPDMaaS) within the Big Data context. The model transparently couples PPDMs with cloud data management. It is based on a stacked architecture that incorporates parallel and distributed processing patterns at design time to efficiently process large-scale volumes of data and create concurrent PPDM processing streams. A prototype of the elastic cloud model was created to validate and evaluate its usability and efficiency under different cryptography-based PPDMs, which supported at least a 128-bit equivalent security level for the most popular data mining tasks: clustering and classification. Validation tests were done using 16 datasets from the UCI repository. In terms of performance, evaluation was done using 50 artificial datasets that resembled a Big Data scenario. The obtained results revealed the efficiency and suitability of the proposed elastic cloud model to enable PPDMaaS. Thus, it guarantees data privacy and reduces processing times mainly induced by homomorphic encryption (one order of magnitude) without affecting the accuracy of obtained data mining models. The novelty of this work is in its elastic and modular design, which enables seamless integration of PPDM methods into a scalable service architecture. The platform provides customizable, efficient, and privacy-preserving data mining capabilities, addressing key limitations of previous approaches and making it suitable for deployment in real-world DMaaS scenarios. • A novel elastic model for privacy preserving data mining as a service. • A flexible, scalable and efficient cloud-based platform for preserving data mining under the big data context.