Sharing without Showing: Secure Cloud Analytics with Trusted Execution Environments

TL;DR

This paper introduces a secure cloud analytics framework using Intel SGX to enable multi-user data aggregation with confidentiality, allowing dynamic user addition and minimal overhead, demonstrated through various data analysis functions.

Contribution

The authors propose a novel system combining trusted execution environments with encrypted data to enable secure, flexible, and efficient multi-user analytics without re-encryption or trusted third parties.

Findings

Average overhead of 1.6x for common functions

Supports dynamic user addition without re-encryption

Securely computes data distributions without revealing individual data

Abstract

Many applications benefit from computations over the data of multiple users while preserving confidentiality. We present a solution where multiple mutually distrusting users' data can be aggregated with an acceptable overhead, while allowing users to be added to the system at any time without re-encrypting data. Our solution to this problem is to use a Trusted Execution Environment (Intel SGX) for the computation, while the confidential data is encrypted with the data owner's key and can be stored anywhere, without trust in the service provider. We do not require the user to be online during the computation phase and do not require a trusted party to store data in plain text. Still, the computation can only be carried out if the data owner explicitly has given permission. Experiments using common functions such as the sum, least square fit, histogram, and SVM classification, exhibit an average overhead of $1.6 \times$. In addition to these performance experiments, we present a use case for computing the distributions of taxis in a city without revealing the position of any other taxi to the other parties.