PrivEx: Private Collection of Traffic Statistics for Anonymous Communication Networks

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Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, methodology): “[86] and has roughly the same cost, in terms of exponentiations per client request, as the “distributed decryption” variant of PrivEx [56].”
Efficient Private Statistics with Succinct Sketches (2015, methodology): “In order to ensure robustness, the Tor project has determined that the median – rather than the mean – of these volumes should be calculated, which is beyond privacy-friendly statistics approaches like Privex [32].”
Distributed Measurement with Private Set-Union Cardinality (2017, background): “An important property of our system — and one that is shared in existing work on privacy-preserving measurement systems [19, 30, 36] — is that the counters be stored obliviously.”
Understanding Tor Usage with Privacy-Preserving Measurement (2018, background): “measurement systems [23, 36]) is that it cannot determine the count of distinct values among the DCs.”
Differentially private data aggregation with optimal utility (2014, background): “Elahi et al. recently proposed PrivEx [24], a system for collecting diﬀerentially private statistics on ACNs traﬃc in predeﬁned slots of time (epochs).”
Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017, methodology): “Pairwise blinding using additive stream ciphers has been explored in previous work [15], [18], [33], [34], and deal with”