Privacy Preserving Elastic Stream Processing with Clouds Using Homomorphic Encryption

Abstract

Prevalence of the Infrastructure as a Service (IaaS) clouds has enabled organizations to elastically scale their stream processing applications to public clouds. However, current approaches for elastic stream processing do not consider the potential security vulnerabilities in cloud environments. In this paper we describe the design and implementation of an Elastic Switching Mechanism for data stream processing which is based on Homomorphic Encryption (HomoESM). The HomoESM not only does elastically scale data stream processing applications into public clouds but also preserves the privacy of such applications. Using a real world test setup, which includes an email filter benchmark and a web server access log processor benchmark (EDGAR) we demonstrate the effectiveness of our approach. Multiple experiments on Amazon EC2 indicate that the proposed approach for Homomorphic encryption provides significant results which is 10% to 17% improvement of average latency in the case of email filter benchmark and EDGAR benchmarks respectively. Furthermore, EDGAR add/subtract operations and comparison operations showed 6.13% and 26.17% average latency improvements respectively. These promising results pave the way for real world deployments of privacy preserving elastic stream processing in the cloud.