Poster: Privacy-Preserving Heat Map Generation through Spatial and Temporal Local Perturbation Toon Dehaene, Michiel Willocx, Bert Lagaisse, Vincent Naessens DistriNet, KU Leuven Belgium firstname.lastname@kuleuven.be Abstract—Businesses and governments increasingly apply ma- chine learning and AI techniques for various strategic opti- mization purposes. Many applications, however, require location data over time of many individuals to train models. Currently, location streams are collected – often without the end-user’s consent or even knowledge – by means of seemingly innocent smartphone applications that continuously report location data in a background process, raising serious privacy concerns. In this poster, we propose a transparent and privacy-friendly approach that perturbs sensitive location data in two dimensions, namely spatially and temporally. The mechanism can be deployed on mobile devices such that all obfuscation is executed locally, thereby protecting the user even in the presence of an untrust- worthy data collector. The viability of the proposed mechanism is demonstrated by generating heat maps and predicting hot spots based on privacy-preserving location streams of many users. Index Terms—Location, Privacy, Obfuscation, Perturbation I. INTRODUCTION Nowadays, many companies and governmental institutions rely on AI and ML models to optimize business goals and soci- etal challenges. These models offer, amongst others, insights in busy areas and crowd movements and aim at optimizing emerging challenges based on the findings. Examples are traffic optimization based on historic data streams, optimizing visitor flows in touristic hot spots, selecting feasible locations to open new shops, bars or restaurants and deliberately assign- ing first aid personnel during events. Currently, location streams are collected and aggregated by data brokers, and subsequently sold to data analysts. Data collection often occurs by means of smartphone applications. Dedicated data collection processes typically run in the back- ground, continuously logging user locations, and subsequently send the data to a remote server. Users are often completely unaware of these practises. Although privacy legislation and technology providers are cracking down on these practices, industry practises show that this type of data is still sold on a massive scale today at various platforms. While collecting personal location data over time is contro- versial and impedes the user privacy, the practical applications of location data are often of a legitimate nature and provide immeasurable value to governments and commercial organi- zations. To improve the privacy/utility balance, this poster proposes a privacy-friendly alternative for collecting location data over time. The mechanism demonstrates that – while a feasible privacy level is reached – crowd insights can still be learned from the obfuscated data. The mechanism performs all obfuscation locally on the user’s device, thereby distrusting data collectors. Contributions. This poster proposes a hybrid mechanism that supports the privacy-preserving collection of location streams. We introduce the concept of automatically generated privacy blobs, which vastly limit the amount of information an individual leaks towards data collectors, even when reporting location data over time. While the location data of each individual separately is meaningless up to a certain privacy level, we demonstrate that aggregating the privacy-enhanced location data still allows the creation of accurate heat maps on a population-level. II. RELATED WORK Many research already focused on location obfuscation. Early work [4], [6] was mostly concerned with obfuscating location datasets of known size. Later works [8] have raised concerns about the continuous release of locations, and provide privacy guarantees for location streams. Our approach benefits from these research results. For location obfuscation, our work builds further