Distributed financial systems are radically changing the way we do business and spend our money. Ripple, in particular, is unique in its kind. It is built on consensus and trust among its users and it allows to exchange both fiat currencies and goods over its network. It does so by storing the accounts of its users, their balances, and all the transactions in a distributed ledger, publicly accessible. In this paper we perform an in-depth study of the Ripple exchange system and its public distributed ledger. We analyze payments, the structure of payment paths, and the role of the entities in the system such as Gateways (the equivalent of banks) and Market Makers. We also analyze the internal stream of events and show that Ripple relies on a surprisingly small number of active validators, raising concerns on the actual robustness and fairness of the system. Moreover, we consider the degree of anonymity that Ripple is able to guarantee. By examining the first three years of Ripple history (more than 500 GB worth of data), we show that even approximate information on a single payment can uncover, with incredible accuracy, the entire financial life of the user. For example, anyone who overhears our order of a Latte at our favourite bar can easily get complete and unlimited access to our balance, our previous and future payments, our monthly income, as well as critical information about the places where we shop and the people we trust.

Whenever our smartphones have their WiFi radio interface on, they periodically try to connect to known wireless APs (networks the user has connected to in the past). This is done through WiFi Probe requests - special wireless frames that contain the MAC address of the sending device and, in most of the cases, the human-readable name-string (SSID) of the known AP. This semantic information, inherent to the network protocol, is sent in the clear and, if sniffed, can help discover important information and phenomena of people and human nature that have nothing to do with technology. In this paper we present the idea of exploiting WiFi probe requests to de-anonymize the origin of participants in large events. We make use of several, publicly available datasets containing more than 11M of probe requests collected in scenarios that are of citywide, national (two political meetings), and international religion-related relevance. We show how, by exploiting the semantic information brought by the relative WiFi probes, we are able to discover with high accuracy the provenance of the crowds in each event. In particular, the de-anonymization outcome of the two political meetings held few days before the election days in Italy match surprisingly well the official voting results reported for the two respective parties.