Researchers Enhance Libertas Synthetic Data Platform with Secure Enclave Integration

Global: Researchers Enhance Libertas Synthetic Data Platform with Secure Enclave Integration

Researchers have introduced a hybrid approach that combines Intel SGX secure enclaves with multi‑party computation (MPC) to improve the scalability of Libertas, a decentralized system for privacy‑preserving synthetic data generation. The integration aims to lower computational and communication costs while maintaining contributor autonomy and differential privacy guarantees.

Background and Motivation

As high‑quality web data becomes increasingly difficult to access, synthetic data has emerged as a promising solution for privacy‑friendly data release and for augmenting real datasets in AI development. However, existing decentralized solutions often struggle with the resource demands of cryptographic protocols.

Libertas Architecture

Libertas leverages Solid Pods—personal, decentralized data stores—and MPC to enable contributors to jointly compute functions over their data without a trusted central authority. This design ensures that individuals retain control over who can access their information while facilitating collaborative synthetic data generation.

Scalability Challenges

Despite its privacy advantages, Libertas faces limited scalability due to the high computation and communication overhead inherent in MPC protocols. These constraints hinder its applicability to large‑scale datasets commonly found on the Web.

Secure Enclave Integration

The proposed enhancement incorporates Intel SGX secure enclaves, which provide hardware‑based confidentiality and integrity for code and data. By offloading selected cryptographic operations to enclaves, the system reduces the burden on MPC, thereby addressing the identified scalability bottleneck.

Empirical Evaluation

Authors evaluated the hybrid architecture on both simulated and real‑world datasets using several synthetic data generation algorithms. Results indicate a measurable decrease in runtime and network traffic, while differential privacy guarantees remain intact.

Implications and Future Directions

The findings suggest that combining secure enclaves with MPC can make decentralized synthetic data platforms more practical for large‑scale deployments. Future research may explore additional hardware‑based security primitives and broader integration with emerging web standards.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.