TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis - Alam, A. K. M. Mubashwir; Chen, Keke (Frontiers in Big Data, 2023)
Big graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs for the key involved parties: data contributors and the data owner who collects data from contributors. We propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Compared to existing confidential graph analysis approaches, our approach has several unique contributions. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers. We have compared our work with PrivateGraph, a software crypto approach for graph spectral analysis. Our experimental results show TEE-Graph has significantly better performance and lower costs than PrivateGraph.like GDPR Zaeem and Barber (2020); Chander et al. ( 2020) also guarantee fine-grained privacy rights, e.g., contributors can withdraw their data from sharing at any time: the right of revocation. These challenges raise the standard for cloud-based graph analytics solutions, which have not been comprehensively addressed by