Aggregation of values that need to be kept confidential while guaranteeing the robustness of the process and the correctness of the result is necessary for an increasing number of applications. Various kinds of surveys, such as medical ones, opinion polls, referendums, elections, as well as new services of the Internet of Things, such as home automation, require the aggregation of confidential data. In general, the confidentiality is ensured on the basis of trusted third parties or promises of cryptography, whose capacities cannot be assessed without expert knowledge.
The ambition of this thesis is to reduce the need for trust in both authorities and technology and explore methods for large-scale data aggregations, that ensure a high degree of confidentiality and rely neither on trusted third parties nor solely on cryptography. Inspired by BitTorrent and Bitcoin, P2P protocols are considered.
The first contribution of this thesis is the extension of the distributed aggregation protocol BitBallot with the objective to cover aggregations in P2P networks comprising adversarial peers with fail-stop or Byzantine behaviour. The introduced changes allow eventually to maintain an accurate result in presence of an adversarial minority.
The encountered scalability limitations lead to the second contribution with the objective to support large-scale aggregations. Inspired by both BitBallot and BitTorrent, a novel distributed protocol called ADVOKAT is proposed.
In both protocols, peers are assigned to leaf nodes of a tree overlay network which determines the computation of intermediate aggregates and restricts the exchange of data. The partition of data and computation among a network of equipotent peers limits the potential for data breaches and reduces the need for trust in authorities. The protocols provide a middleware layer whose flexibility is demonstrated by voting and lottery applications.