Understanding Tor Usage with Privacy-Preserving Measurement

By Akshaya Mani, T. Wilson-Brown, Rob Jansen, Aaron Johnson, Michael E. Sherr. 2018

90 citations recorded by the source corpus.

The Tor anonymity network is difficult to measure because, if not done carefully, measurements could risk the privacy (and potentially the safety) of the network's users. Recent work has proposed the use of differential privacy and secure aggregation techniques to safely measure Tor, and preliminary proof-of-concept prototype tools have been developed in order to demonstrate the utility of these techniques. In this work, we significantly enhance two such tools---PrivCount and Private Set-Union Cardinality---in order to support the safe exploration of new types of Tor usage behavior that have never before been measured. Using the enhanced tools, we conduct a detailed measurement study of Tor covering three major aspects of Tor usage: how many users connect to Tor and from where do they connect, with which destinations do users most frequently communicate, and how many onion services exist and how are they used. Our findings include that Tor has ~8 million daily users, a factor of four more than previously believed. We also find that ~40% of the sites accessed over Tor have a torproject.org domain name, ~10% of the sites have an amazon.com domain name, and ~80% of the sites have a domain name that is included in the Alexa top 1 million sites list. Finally, we find that ~90% of lookups for onion addresses are invalid, and more than 90% of attempted connections to onion services fail.

Subfields: Artificial Intelligence, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chengfei Lv
2. Shaojie Tang
3. Guihai Chen
4. Fan Wu
5. Chaoyue Niu
6. Lifeng Hua
7. Rongfei Jia
8. Zhihua Wu

Citations in the corpus

• Secure Federated Submodel Learning (2019, background): “Compared with Private Set Intersection (PSI) [71]–[76] and Private Set union Cardinality (PSC) [77]– [79], which have received tremendous attention and also have seen several practical applications, such as in social networks [80], [81]; human genome testing [82]; locationbased services [83]; securi”