We find that 84\% of paths originating in Brazil traverse the United States, but when relays are used for country avoidance, only 37\% of Brazilian paths traverse the United States. Using the open DNS resolver infrastructure allows Kenyan clients to avoid the United States on 17\% more paths. Unfortunately, we find that some of the more prominent surveillance states (e.g., the U.S.) are also some of the least avoidable countries.
Perfect anonymization of data sets that contain personal information has failed. But the process of protecting data subjects in shared information remains integral to privacy practice and policy. While the deidentification debate has been vigorous and productive, there is no clear direction for policy. As a result, the law has been slow to adapt a holistic approach to protecting data subjects when data sets are released to others. Currently, the law is focused on whether an individual can be identified within a given set. We argue that the best way to move data release policy past the alleged failures of anonymization is to focus on the process of minimizing risk of reidentification and sensitive attribute disclosure, not preventing harm. Process-based data release policy, which resembles the law of data security, will help us move past the limitations of focusing on whether data sets have been “anonymized.” It draws upon different tactics to protect the privacy of data subjects, including accurate deidentification rhetoric, contracts prohibiting reidentification and sensitive attribute disclosure, data enclaves, and query-based strategies to match required protections with the level of risk. By focusing on process, data release policy can better balance privacy and utility where nearly all data exchanges carry some risk.
paper here
These nodes — ordinary nodes, not exit nodes — sorted through all the traffic that passed through them, looking for anything bound for a hidden service, which allowed them to discover hidden services that had not been advertised. These nodes then attacked the hidden services by making connections to them and trying common exploits against the server-software running on them, seeking to compromise and take them over.
The researchers used “honeypot” .onion servers to find the spying computers: these honeypots were .onion sites that the researchers set up in their own lab and then connected to repeatedly over the Tor network, thus seeding many Tor nodes with the information of the honions’ existence. They didn’t advertise the honions’ existence in any other way and there was nothing of interest at these sites, and so when the sites logged new connections, the researchers could infer that they were being contacted by a system that had spied on one of their Tor network circuits.
These nodes — ordinary nodes, not exit nodes — sorted through all the traffic that passed through them, looking for anything bound for a hidden service, which allowed them to discover hidden services that had not been advertised. These nodes then attacked the hidden services by making connections to them and trying common exploits against the server-software running on them, seeking to compromise and take them over.
The researchers used “honeypot” .onion servers to find the spying computers: these honeypots were .onion sites that the researchers set up in their own lab and then connected to repeatedly over the Tor network, thus seeding many Tor nodes with the information of the honions’ existence. They didn’t advertise the honions’ existence in any other way and there was nothing of interest at these sites, and so when the sites logged new connections, the researchers could infer that they were being contacted by a system that had spied on one of their Tor network circuits.