A lightweight MapReduce framework for secure processing with SGX
This addresses security concerns for cloud-based MapReduce processing, offering a more efficient solution for users handling sensitive data, though it is incremental as it applies an existing hardware feature to a known framework.
The paper tackled the problem of providing privacy guarantees for MapReduce operations in cloud environments by using Intel SGX hardware extensions, concluding it is a viable alternative to cryptographic methods with reduced computational overhead.
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.