Decentralized Policy-Based Private Analytics
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We are increasingly surrounded by applications, connected devices, services, and smart environments which require fine-grained access to various personal data. The inherent complexities of our personal and professional policies and preferences in interactions with these analytics services raise important challenges in privacy. Moreover, due to sensitivity of the data and regulatory and technical barriers, it is not always feasible to do these policy negotiations in a centralized manner. In this paper we present PoliBox, a decentralized, edge-based framework for policy-based personal data analytics. PoliBox brings together a number of existing established components to provide privacy-preserving analytics within a distributed setting. We evaluate our framework using a popular exemplar of private analytics, Federated Learning, and demonstrate that for varying model sizes and use cases, PoliBox is able to perform accurate model training and inference within very reasonable resource and time budgets.
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