Libertas: Privacy-Preserving Collaborative Computation for Decentralised Personal Data Stores

Data and their processing have become an indispensable aspect for our society. Insights drawn from collective data make invaluable contribution to scientific, societal and communal research and business. However, there are increasing worries about privacy issues and data misuse, prompting the emergence of decentralised personal data stores (PDS) like Solid. However, existing PDS frameworks face challenges in ensuring data privacy when performing collective computation to combine data from multiple users. At a glance, Secure Multi-Party Computation (MPC) offers input secrecy protection while performing collective computation without relying on any single party. However, issues emerge when directly applying MPC in the context of PDS, particularly due to key factors like autonomy and decentralisation. In this work, we discuss the essence of this issue, identify the potential solution, and introduce a modular system architecture, Libertas, to integrate MPC with PDS like Solid, without requiring protocol-level changes. We introduce the paradigm shift from an 'omniscient' view to individual-based, user-centric view of trust and security, and discuss the threat model of Libertas. Two realistic use cases for collaborative data processing are used for evaluation, both for technical feasibility and empirical benchmark, highlighting its effectiveness in empowering gig workers and generating differentially private synthetic data. The results of our experiments underscore Libertas' linear scalability and provide valuable insights into compute optimisations, thereby advancing the state-of-the-art in privacy-preserving data processing practices. By offering practical solutions for maintaining both individual autonomy and privacy in collaborative data processing environments, Libertas contributes significantly to the ongoing discourse on privacy protection in data-driven decision-making contexts.