The Real First Class? Inferring Confidential Corporate Mergers and Government Relations from Air Traffic Communication

This paper exploits publicly available aircraft meta data in conjunction with unfiltered air traffic communication gathered from a global collaborative sensor network to study the privacy impact of large-scale aircraft tracking on governments and public corporations. First, we use movement data of 542 verified aircraft used by 113 different governments to identify events and relationships in the real world. We develop a spatio-temporal clustering method which returns 47 public and 18 non-public meetings attended by dedicated government aircraft over the course of 18 months. Additionally, we illustrate the ease of analyzing the long-term behavior and relationships of aviation users through the example of foreign governments visiting Europe. Secondly, we exploit the same types of data to predict potential merger and acquisition (M&A) activities by 36 corporations listed on the US and European stock markets. We identify seven M&A cases, in all of which the buyer has used corporate aircraft to visit the target prior to the official announcement, on average 61 days before. Finally, we analyze five existing technical and non-technical mitigation options available to the individual stakeholders. We quantify their popularity and effectiveness, finding that despite their current widespread use, they are ineffective against the presented exploits. Consequently, we argue that regulatory and technical changes are required to be able to protect the privacy of non-commercial aviation users in the future.