Distributed anonymous data collection and feedback
Maxim Timchenko, Ari Trachtenberg · 2015
Diagnostic, usage, and statistical data collection occurs continuously in the background on our computers and smart devices. However, the privacy and anonymity of the process or of the resulting data set are seldom given much thought by device owners. We propose and are in the process of implementing and evaluating a framework for non-realtime anonymous data collection, aggregation for analysis, and feedback. Departing from the usual "trusted core" approach, we aim to maintain the reporter's anonymity, even if the core of the system is compromised. We design a peer-to-peer mix network tuned to carry data to a centralized repository while maintaining (i) source anonymity, (ii) privacy in transit, (iii) the ability to provide feedback from central server to source.