Improving Data Minimization through Decentralized Data Architectures

In this research project, we investigate an alternative to the standard cloud-centralized data architecture. Specifically, we aim to leave part of the application data under the control of the individual data owners in decentralized personal data stores. Our primary goal is to increase data minimization, i. e., enabling more sensitive personal data to be under the control of its owners while providing a straightforward and efficient framework to design architectures that allow applications to run and data to be analyzed. To serve this purpose, the centralized part of the schema contains aggregating views over this decentralized data. We propose to design a declarative language that extends SQL, for architects to specify different kinds of tables and views at the schema level, along with sensitive columns and their minimum granularity level of their aggregations. Local updates need to be reflected in the centralized views while ensuring privacy throughout intermediate calculations; for this we pursue the integration of distributed materialized view maintenance and multi-party computation (MPC) techniques. We finally aim to implement this system, where the personal data stores could either live in mobile devices or encrypted cloud storage, in order to evaluate its performance properties.