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Open access

Steven Englehardt, Jeffrey Han and Arvind Narayanan

Abstract

We show that the simple act of viewing emails contains privacy pitfalls for the unwary. We assembled a corpus of commercial mailing-list emails, and find a network of hundreds of third parties that track email recipients via methods such as embedded pixels. About 30% of emails leak the recipient’s email address to one or more of these third parties when they are viewed. In the majority of cases, these leaks are intentional on the part of email senders, and further leaks occur if the recipient clicks links in emails. Mail servers and clients may employ a variety of defenses, but we analyze 16 servers and clients and find that they are far from comprehensive. We propose, prototype, and evaluate a new defense, namely stripping tracking tags from emails based on enhanced versions of existing web tracking protection lists.

Open access

Steven Goldfeder, Harry Kalodner, Dillon Reisman and Arvind Narayanan

Abstract

We show how third-party web trackers can deanonymize users of cryptocurrencies. We present two distinct but complementary attacks. On most shopping websites, third party trackers receive information about user purchases for purposes of advertising and analytics. We show that, if the user pays using a cryptocurrency, trackers typically possess enough information about the purchase to uniquely identify the transaction on the blockchain, link it to the user’s cookie, and further to the user’s real identity. Our second attack shows that if the tracker is able to link two purchases of the same user to the blockchain in this manner, it can identify the user’s cluster of addresses and transactions on the blockchain, even if the user employs blockchain anonymity techniques such as CoinJoin. The attacks are passive and hence can be retroactively applied to past purchases. We discuss several mitigations, but none are perfect.

Open access

Malte Möser, Kyle Soska, Ethan Heilman, Kevin Lee, Henry Heffan, Shashvat Srivastava, Kyle Hogan, Jason Hennessey, Andrew Miller, Arvind Narayanan and Nicolas Christin

Abstract

Monero is a privacy-centric cryptocurrency that allows users to obscure their transactions by including chaff coins, called “mixins,” along with the actual coins they spend. In this paper, we empirically evaluate two weaknesses in Monero’s mixin sampling strategy. First, about 62% of transaction inputs with one or more mixins are vulnerable to “chain-reaction” analysis - that is, the real input can be deduced by elimination. Second, Monero mixins are sampled in such a way that they can be easily distinguished from the real coins by their age distribution; in short, the real input is usually the “newest” input. We estimate that this heuristic can be used to guess the real input with 80% accuracy over all transactions with 1 or more mixins. Next, we turn to the Monero ecosystem and study the importance of mining pools and the former anonymous marketplace AlphaBay on the transaction volume. We find that after removing mining pool activity, there remains a large amount of potentially privacy-sensitive transactions that are affected by these weaknesses. We propose and evaluate two countermeasures that can improve the privacy of future transactions.