Biblio

With the emergence of the internet of things (IoT) and participatory sensing (PS) paradigms trustworthiness of remotely sensed data has become a vital research question. In this work, we present the design of a trusted sensor, which uses physically unclonable functions (PUFs) as anchor to ensure integrity, authenticity and non-repudiation guarantees on the sensed data. We propose trusted sensors for mobile devices to address the problem of potential manipulation of mobile sensors' readings by exploiting vulnerabilities of mobile device OS in participatory sensing for IoT applications. Preliminary results from our implementation of trusted visual sensor node show that the proposed security solution can be realized without consuming significant amount of resources of the sensor node.

With the emergence of the internet of things (IoT) and participatory sensing (PS) paradigms trustworthiness of remotely sensed data has become a vital research question. In this work, we present the design of a trusted sensor, which uses physically unclonable functions (PUFs) as anchor to ensure integrity, authenticity and non-repudiation guarantees on the sensed data. We propose trusted sensors for mobile devices to address the problem of potential manipulation of mobile sensors' readings by exploiting vulnerabilities of mobile device OS in participatory sensing for IoT applications. Preliminary results from our implementation of trusted visual sensor node show that the proposed security solution can be realized without consuming significant amount of resources of the sensor node.

Participatory sensing tries to create cost-effective, large-scale sensing systems by leveraging sensors embedded in mobile devices. One major challenge in these systems is to protect the users' privacy, since users will not contribute data if their privacy is jeopardized. Especially location data needs to be protected if it is likely to reveal information about the users' identities. A common solution is the blinding out approach that creates so-called ban zones in which location data is not published. Thereby, a user's important places, e.g., her home or workplace, can be concealed. However, ban zones of a fixed size are not able to guarantee any particular level of privacy. For instance, a ban zone that is large enough to conceal a user's home in a large city might be too small in a less populated area. For this reason, we propose an approach for dynamic map-based blinding out: The boundaries of our privacy zones, called Silent Zones, are determined in such way that at least k buildings are located within this zone. Thus, our approach adapts to the habitat density and we can guarantee k-anonymity in terms of surrounding buildings. In this paper, we present two new algorithms for creating Silent Zones and evaluate their performance. Our results show that especially in worst case scenarios, i.e., in sparsely populated areas, our approach outperforms standard ban zones and guarantees the specified privacy level.