Privacy-Preserving Edge Processing in Decentralized Citizen-Centric Sensor Networks

The number of people living in cities is growing every year. This leads to challenges such as scarcity of natural resources, demographic change, and ongoing urbanization, to which smart cities promise a solution. With the help of the growing Internet of Things, these visions could become reality. Already there are many sensors deployed in local households but collected data streams are often used only locally. One of the main concerns of sharing collected data with external services and applications is the citizen’s privacy. Citizens only want to share data that is truly necessary for the service, and not all data they collect. This work presents an approach that reduces data collection to the required minimum within citizen centric sensor network. Therefore services requesting citizens’ data define preprocessing steps that are executed locally at the citizens’ devices. Preprocessed data is then sent to the service, reducing network load and increasing citizens’ privacy by only sending relevant information. A major challenge is to describe these preprocessing steps in a comprehensible manner. This ensures that they can be understood by average citizens enabling them to make informed decisions. In particular, it must be ensured that only data specified within the steps is sent to the service. To evaluate our approach regarding comprehensibility and usability a prototype has been implemented and used within a user study. The results show that for the majority of tasks, both users with and without computer science backgrounds were able to understand the given preprocessing step sequences. Furthermore, developers were able to define preprocessing step sequences themselves.