Current applications, from complex sensor systems (e.g., quantified self) to online e-markets, acquire vast quantities of personal information that usually end up on central servers where they are exposed to prying eyes. Conversely, decentralized architectures that help individuals keep full control of their data complexify global treatments and queries, impeding the development of innovative services. This article aims precisely at reconciling individual's privacy on one side and global benefits for the community and business perspectives on the other. It promotes the idea of pushing the security to secure hardware devices controlling the data at the place of their acquisition. Thanks to these tangible physical elements of trust, secure distributed querying protocols can reestablish the capacity to perform global computations, such as Structured Query Language (SQL) aggregates, without revealing any sensitive information to central servers. This article studies how to secure the execution of such queries in the presence of honest-but-curious and malicious attackers. It also discusses how the resulting querying protocols can be integrated in a concrete decentralized architecture. Cost models and experiments on SQL/Asymmetric Architecture (AA), our distributed prototype running on real tamper-resistant hardware, demonstrate that this approach can scale to nationwide applications.

Current applications, from complex sensor systems (e.g. quantified self) to online e-markets acquire vast quantities of personal information which usually ends-up on central servers. Decentralized architectures, devised to help individuals keep full control of their data, hinder global treatments and queries, impeding the development of services of great interest. This paper promotes the idea of pushing the security to the edges of applications, through the use of secure hardware devices controlling the data at the place of their acquisition. To solve this problem, we propose secure distributed querying protocols based on the use of a tangible physical element of trust, reestablishing the capacity to perform global computations without revealing any sensitive information to central servers. There are two main problems when trying to support SQL in this context: perform joins and perform aggregations. In this paper, we study the subset of SQL queries without joins and show how to secure their execution in the presence of honest-but-curious attackers. Cost models and experiments demonstrate that this approach can scale to nationwide infrastructures.

Current applications, from complex sensor systems (e.g. quantified self) to online e-markets acquire vast quantities of personal information which usually end-up on central servers. This information represents an unprecedented potential for user customized applications and business (e.g., car insurance billing, carbon tax, traffic decongestion, resource optimization in smart grids, healthcare surveillance, participatory sensing). However, the PRISM affair has shown that public opinion is starting to wonder whether these new services are not bringing us closer to science fiction dystopias. It has become clear that centralizing and processing all one's data on a single server is a major problem with regards to privacy concerns. Conversely, decentralized architectures, devised to help individuals keep full control of their data, complexify global treatments and queries, often impeding the development of innovative services and applications.