Latency-Aware Secure Elastic Stream Processing with Homomorphic Encryption

Abstract

Abstract Increasingly organizations are elastically scaling their stream processing applications into the infrastructure as a service clouds. However, state-of-the-art approaches for elastic stream processing do not consider the potential threats of exposing their data to third parties in cloud environments. We present the design and implementation of an Elastic Switching Mechanism for data stream processing which is based on homomorphic encryption (HomoESM). The HomoESM not only elastically scales data stream processing applications into public clouds but also preserves the privacy of such applications. Using a real-world test setup, which includes an E-mail Filter benchmark and a Web server access log processor benchmark (EDGAR), we demonstrate the effectiveness of our approach. Experiments on Amazon EC2 indicate that the proposed approach for homomorphic encryption provides a significant result which is 10–17% improvement in average latency in the case of E-mail Filter benchmark and EDGAR benchmark, respectively. Furthermore, EDGAR add/subtract operations, multiplication, and comparison operations showed up to 6.13%, 7.81%, and 26.17% average latency improvements, respectively. Finally, we evaluate the potential of scaling the homomorphic stream processor in the public cloud. These results indicate the potential for real-world deployments of secure elastic data stream processing applications.

Keywords

• Cloud computing
• Elastic data stream processing
• Compressed event processing
• Data compression
• IaaS
• System sizing and capacity planning