Secure Location Semantics

Introduction

We have a working prototype to detect other nearby devices in the same environment. Therefore, we use WiFi signals, ambient audio similarity and ultrasound in periods of silence. As a result, we are able to autonomously group devices based on their perceived environment. The goal of the master thesis is to secure this proximity system in terms of communication and user privacy.

Details

The network transmission between the peer-to-peer clients must be protected when we send sensor data to the temporary server for proximity reasoning. Therefore, a lightweight encryption is preferable to secure the communication channel due to the hardware constraints of mobile devices, like Speck ciphers instead of AES.

Initially, the proximity system uses WiFi Direct to advertise services via broadcast messages, which can be received by any device, which is in range of the broadcasting device. An idea towards a passive system which collects nearby data, but is not actively advertising and discoverable by other, maybe malicious entities, would be to limit the WiFi Direct advertising to specific areas determined by Bluetooth Low Energy (BLE) beacons. We can leverage our existing testbed with 50 BLE beacons and MongoDB backend to implement this functionality.

Besides that, other services are enabled when we know the specific room of a group of users (not part of the master thesis) [1]:

  • Room-restricted navigation to next printer
  • WiFi Authorization scheme where only users in a certain room area able to connect
  • Rate limits and traffic prioritization for certain rooms
  • Spaced-based access control and authorization for building automation

Goals and Objectives

  • Identify possible attack points of the proximity system [2]
  • Space limited active service advertising (WiFi Direct)
  • Secure communication between P2P clients regarding
    • Lightweight encryption, such as Simon and Speck
    • Automatic key distribution
  • Mobile client controls release of sensor data to temporary server for fusion of proximity reasoning [3]

References

[1] Iannuci et al., Room-Area Networks, Proceedings of the 14th ACM Workshop on Hot Topics in Networks (HotNets), 2015, Link: http://dl.acm.org/citation.cfm?id=2834113

[2] Shrestha et al., Contextual Proximity Detection in the Face of Context-Manipulating Adversaries, arxiv, 2015, Link: arxiv.org/abs/1511.00905

[3] Haus et al., P2Hub: Privacy Personal Data Hub for Mobile Devices, Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2016, Link: http://dl.acm.org/citation.cfm?id=2942403

Contact

Michael Haus (M.Sc.), haus at in tum de