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Data-driven decision-making and AI applications present exciting newopportunities delivering widespread benefits. The rapid adoption of suchapplications triggers legitimate concerns about loss of privacy and misuse ofpersonal data. This leads to a growing and pervasive tension between harvestingubiquitous data on the Web and the need to protect individuals. Decentralisedpersonal data stores (PDS) such as Solid are frameworks designed to giveindividuals ultimate control over their personal data. But current PDSapproaches have limited support for ensuring privacy when computations combinedata spread across users. Secure Multi-Party Computation (MPC) is a well-knownsubfield of cryptography, enabling multiple autonomous parties tocollaboratively compute a function while ensuring the secrecy of inputs (inputprivacy). These two technologies complement each other, but existing practicesfall short in addressing the requirements and challenges of introducing MPC ina PDS environment. For the first time, we propose a modular design forintegrating MPC with Solid while respecting the requirements ofdecentralisation in this context. Our architecture, Libertas, requires noprotocol level changes in the underlying design of Solid, and can be adapted toother PDS. We further show how this can be combined with existing differentialprivacy techniques to also ensure output privacy. We use empirical benchmarksto inform and evaluate our implementation and design choices. We show thetechnical feasibility and scalability pattern of the proposed system in twonovel scenarios -- 1) empowering gig workers with aggregate computations ontheir earnings data; and 2) generating high-quality differentially-privatesynthetic data without requiring a trusted centre. With this, we demonstratethe linear scalability of Libertas, and gained insights about computeoptimisations under such an architecture.

Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores