Statistical Approach to Estimating Audience from MAC-Randomized WiFi Probe Requests

Abstract

In the past few years, the ability of wireless network operators to monitor audience using control frames emitted by client devices has been compromised, both by legislation treating client MAC addresses as private information and by the difficulty of distinguishing genuine client frames from those arising from the Internet of Things or from certain enhanced services. Here, a deterministic model, based on characteristics of human activity and on seasonal trends, is used to reveal underlying client statistics in raw MAC-randomized WiFi Probe Request data. The method proposes a candidate conversion factor, X, between probe request counts and the client population, which offers plausible predictions on real-world datasets.

Tools for Ground-Truth-Free Passive Client Density Mapping in MAC-Randomized Outdoor WiFi Networks

“…Again, as introduced in Section 1.4, it has recently been demonstrated theoretically in [1,6] and observed empirically in [1,6,8] that due to the statistical independence of the client density and client device emission probability distributions, a simple proportionality exists, in the limit of a large number of clients, between the number of randomized MAC clients, C, and the corresponding number of randomized MAC PR, P, that is, C = P/X, where the proportionality constant X is understood to be the mean device PR emission probability for the time window concerned. This observation is currently the key to understanding client numbers and client densities in the era of data privacy.…”

“…A consensus is growing that data privacy is here to stay and that researchers would do better to concentrate on intrinsically non-device-specific privacy-preserving approaches. Indeed, this is the philosophy adopted in the present work as well as in other recent contributions [1,[6][7][8].…”

“…Indeed, the authors of [5] report a fivefold increase in the percentage of randomized MAC clients, from 10% to 50%, between the years 2016 and 2018. In [1], fixed-MAC clients in 2020-2021 are found not only to represent less than 10% of all PRs, but also to have significantly different PR statistics compared to randomized MAC clients. This latter effect is explained by the fact that a growing fraction of fixed MAC devices correspond either to Internet of Things (IoT) devices or to human clients engaged in extended services such as Peer-to-Peer, both of which produce uncharacteristically large numbers of PR [1].…”

“…It is found that for sufficiently large client populations, the number of PRs is linearly proportional to the true number of clients. The problem remaining then amounts to determining the appropriate proportionality constant, which can be achieved either by comparison to a ground truth obtained from other sensors, such as cameras, entry monitors, etc., as in [6][7][8], or without ground truth, albeit with reduced accuracy, by exploiting observed periodicities in plots of PR counts versus time [1]. This type of approach forms the starting point for the mobile client localization techniques that will be developed in the present work.…”

“…Localization is typically performed by leveraging the physical properties of a client's WiFi signals, such as the Received Signal Strength (RSS), at several network Access Points (APs). Most systems make use of the abundant Probe Request (PR) frames emitted regularly by client devices even when not connected to a network [1][2][3][4][5]. Sophisticated WiFi client localization interfaces have now become commonplace worldwide.…”