The Internet of Things (IoT) shows a clear shift towards analyzing streaming data (collected by IoT devices) using so-called stream processing systems (SPSs) that infer knowledge from these data in (near) real-time. Such SPSs work on the notion of events detected from sensor data, e.g., a user is standing, jogging, or eating.

The SPSs raise serious privacy concerns, as they not only ignore user privacy but also pose new threats to it. For example, a sequence of seemingly nonsensitive events, like "swallow" --> "drink" --> "lay down", can reveal a sensitive private pattern of taking medicine.

A few privacy-preserving mechanisms (PPMs) exist to address the private patterns' threat, but they need to be validated on realistic datasets containing a number of private patterns that are captured by various sensor data, e.g., IMU, ECG/EMG, and heart rate. To date such datasets do not exist. Hence, collecting one would the main goal of this thesis, which will be a big step towards validating existing and designing new PPMs that tackle the threat of private patterns in SPSs.

The precise topic addressing the above research goal would be tailored depending on your skillset. However, a hands-on experience with data collection using smart devices (phones, watches, IoT sensors) and/or user studies is a strong plus. [1, 2] are exemplary data collection studies, which can serve as an inspiration for this work.

[1] FallAllD: An Open Dataset of Human Falls and Activities of Daily Living for Classical and Deep Learning Applications

[2] Case Studies Using Shimmer Sensors