# A lightweight MapReduce framework for secure processing with SGX

## TL;DR

This paper explores using Intel SGX hardware extensions to enhance security in MapReduce frameworks, demonstrating its viability as a privacy-preserving alternative to cryptographic solutions for data processing tasks like k-means clustering.

## Contribution

The paper introduces a lightweight MapReduce framework leveraging Intel SGX for secure processing, providing a practical alternative to cryptographic methods for privacy guarantees.

## Key findings

- SGX-based MapReduce offers comparable security with lower overhead
- Implementation successfully applies to k-means clustering
- Framework can be generalized to other MapReduce applications

## Abstract

MapReduce is a programming model used extensively for parallel data processing in distributed environments. A wide range of algorithms were implemented using MapReduce, from simple tasks like sorting and searching up to complex clustering and machine learning operations. Many of these implementations are part of services externalized to cloud infrastructures. Over the past years, however, many concerns have been raised regarding the security guarantees offered in such environments. Some solutions relying on cryptography were proposed for countering threats but these typically imply a high computational overhead. Intel, the largest manufacturer of commodity CPUs, recently introduced SGX (software guard extensions), a set of hardware instructions that support execution of code in an isolated secure environment. In this paper, we explore the use of Intel SGX for providing privacy guarantees for MapReduce operations, and based on our evaluation we conclude that it represents a viable alternative to a cryptographic mechanism. We present results based on the widely used k-means clustering algorithm, but our implementation can be generalized to other applications that can be expressed using MapReduce model.