Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores

• URL: http://arxiv.org/abs/2309.16365v1
• Date: Thu, 28 Sep 2023 12:07:40 GMT
• Title: Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores
• Authors: Rui Zhao, Naman Goel, Nitin Agrawal, Jun Zhao, Jake Stein, Ruben Verborgh, Reuben Binns, Tim Berners-Lee, Nigel Shadbolt,
• Abstract summary: We propose a modular design for integrating Secure Multi-Party Computation with Solid. Our architecture, Libertas, requires no protocol level changes in the underlying design of Solid. We show how this can be combined with existing differential privacy techniques to also ensure output privacy.
• Score: 19.54818218429241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven decision-making and AI applications present exciting new opportunities delivering widespread benefits. The rapid adoption of such applications triggers legitimate concerns about loss of privacy and misuse of personal data. This leads to a growing and pervasive tension between harvesting ubiquitous data on the Web and the need to protect individuals. Decentralised personal data stores (PDS) such as Solid are frameworks designed to give individuals ultimate control over their personal data. But current PDS approaches have limited support for ensuring privacy when computations combine data spread across users. Secure Multi-Party Computation (MPC) is a well-known subfield of cryptography, enabling multiple autonomous parties to collaboratively compute a function while ensuring the secrecy of inputs (input privacy). These two technologies complement each other, but existing practices fall short in addressing the requirements and challenges of introducing MPC in a PDS environment. For the first time, we propose a modular design for integrating MPC with Solid while respecting the requirements of decentralisation in this context. Our architecture, Libertas, requires no protocol level changes in the underlying design of Solid, and can be adapted to other PDS. We further show how this can be combined with existing differential privacy techniques to also ensure output privacy. We use empirical benchmarks to inform and evaluate our implementation and design choices. We show the technical feasibility and scalability pattern of the proposed system in two novel scenarios -- 1) empowering gig workers with aggregate computations on their earnings data; and 2) generating high-quality differentially-private synthetic data without requiring a trusted centre. With this, we demonstrate the linear scalability of Libertas, and gained insights about compute optimisations under such an architecture.

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