  Cellular providers and data aggregating companies crowdsource celluar signal
strength measurements from user devices to generate signal maps, which can be
used to improve network performance. Recognizing that this data collection may
be at odds with growing awareness of privacy concerns, we consider obfuscating
such data before the data leaves the mobile device. The goal is to increase
privacy such that it is difficult to recover sensitive features from the
obfuscated data (e.g. user ids and user whereabouts), while still allowing
network providers to use the data for improving network services (i.e. create
accurate signal maps). To examine this privacy-utility tradeoff, we identify
privacy and utility metrics and threat models suited to signal strength
measurements. We then obfuscate the measurements using several preeminent
techniques, spanning differential privacy, generative adversarial privacy, and
information-theoretic privacy techniques, in order to benchmark a variety of
promising obfuscation approaches and provide guidance to real-world engineers
who are tasked to build signal maps that protect privacy without hurting
utility. Our evaluation results, based on multiple, diverse, real-world signal
map datasets, demonstrate the feasibility of concurrently achieving adequate
privacy and utility, with obfuscation strategies which use the structure and
intended use of datasets in their design, and target average-case, rather than
worst-case, guarantees.
