Elastic and Secure Energy Forecasting in Cloud Environments

TL;DR

This paper proposes an approach combining elastic stream processing with secure enclaves to enhance energy data analysis in cloud environments, addressing current limitations in elasticity and privacy protection.

Contribution

It introduces a novel framework integrating StreamMine3G with Intel SGX for elastic and secure energy data processing in the cloud.

Findings

Demonstrates the feasibility of combining elasticity and security in stream processing.

Identifies key challenges in implementing SGX-based secure stream processing.

Proposes solutions for privacy-preserving energy data analysis.

Abstract

Although cloud computing offers many advantages with regards to adaption of resources, we witness either a strong resistance or a very slow adoption to those new offerings. One reason for the resistance is that (i) many technologies such as stream processing systems still lack of appropriate mechanisms for elasticity in order to fully harness the power of the cloud, and (ii) do not provide mechanisms for secure processing of privacy sensitive data such as when analyzing energy consumption data provided through smart plugs in the context of smart grids. In this white paper, we present our vision and approach for elastic and secure processing of streaming data. Our approach is based on StreamMine3G, an elastic event stream processing system and Intel's SGX technology that provides secure processing using enclaves. We highlight the key aspects of our approach and research challenges when using Intel's SGX technology.