Practical Fault-Tolerant Data Aggregation
This addresses the problem of improving accuracy and efficiency in fault-tolerant data aggregation for systems with limited resources, though it involves incremental model relaxation.
The paper demonstrates that an existing fault-tolerant data aggregation protocol provides unacceptably low accuracy for many real-life parameters, and presents a new protocol that offers provable security with less computation and better suitability for resource-limited systems like sensor networks.
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 precise analytic and experimental methods. Additionally, we present a precise data aggregation protocol that provides provable level of security even 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.