During Financial Cryptography 2012 Chan et al. presented a novel privacy-protection fault-tolerant data aggregation protocol. Comparing to previous work, their scheme guaranteed provable privacy of individuals and could work even if some number of users refused to participate.In our paper we demonstrate that despite its merits, their method provides unacceptably low accuracy of aggregated data for a wide range of assumed parameters and cannot be used in majority of real-life systems. To show this we use both analytic and experimental methods. Additionally, we present a precise data aggregation protocol that provides provable level of security even when facing massive failures of nodes. Moreover, the protocol requires significantly less computation (limited exploiting of heavy cryptography) than most of currently known fault tolerant aggregation protocols and offers better security guarantees that make it suitable for systems of limited resources (including sensor networks). To obtain our result we relax however the model and allow some limited communication between the nodes.
In our article, we present several protocols that allow to efficiently construct large groups of users based only on local relations of trust. What is more, our approach proves to need only very small computational and communication overhead. Moreover, we give guarantees that a trusted core of the network is defended, even facing a powerful adversary capable of controlling a vast majority of users. This is non-trivial property in real-life networks, as those are usually modelled using preferential attachment graphs, which are extremely prone to attacks on the hub nodes. We show that using our protocols we can achieve similar robustness as Erdős–Renyí graphs, which, on the contrary, are very resistant against attacks focused on chosen nodes. Our protocols have been tested on graphs representing real-world social networks using high performance computing due to the size of the networks. In addition for some protocols, we provided a formal analysis to prove some phenomena in random graphs following power-law distribution, which we use as a network model. Finally, we explicitly demonstrate how our approach can be used to amplify security offered by some privacy-preserving protocols. We believe however that our results can be also seen as a contribution to fundamental observation about the nature of social networks. These results may help to design protocols, whenever it is necessary to gather a big group of users in highly dynamic or even adversarial settings.
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