Spatial-Provenance in Privacy-Constrained Wireless Networks

Objectives of the Project

• In Vehicle-to-Everything (V2X) networks that involve multi-hop communication, the Road Side Units (RSUs) typically desire to gather the location information of the participating vehicles to provide security and network-diagnostics features. 

• Although Global Positioning System (GPS) based localization is widely used by vehicles for navigation; they may not forward their exact GPS coordinates to the RSUs due to privacy issues. 

• To balance the high-localization requirements of RSU and the privacy of the vehicles, we demonstrate a new spatial-provenance framework.

• In this scheme vehicles agree to compromise their privacy to a certain extent and share a low-precision variant of its coordinates in agreement with the demands of the RSU. 

Highlights

• A methodology that can retrieve pathways and locations while adhering to the privacy requirements of the nodes with a minimal number of operations. 

• A novel methodology with linear modelling on the roads. 

• Flexibility on privacy by dividing the roads into equal segments.

• Features a practical setup of ZigBee and LoRa devices and implements the proposed protocols on their stack using correlated Bloom filters and Rake compression algorithms. 

• Spatial provenance is embedded in a payload segment of the packet, allowing for its integration into any protocol. 

• Our protocol is adaptable to the maximum wireless transmission range of the underlying vehicles.

Importance of Spatial-Provenance

By sharing the location information both the parties involved in communication, i.e., the vehicle and the RSU will have benefits. 

Linear Modeling of Roads and Segmentation

Testbed Description 

Our testbed is built using a network of Raspberry Pis, Digi XBee wireless devices, LoRa nodes and a few high-performance computing devices. The specific roles of these devices are listed below: 

• The role of RSU is played by a high-performance computing device, which is connected to an XBee and LoRa device to receive the packets from the vehicles. It is given higher computation capability as it has to perform various tasks during the provenance recovery process.

• The role of a vehicle is played by a combination of Raspberry Pi, XBee\LoRa devices and GPS modules. The former is used for computing purposes, whereas the XBee/LoRa is used for communication purposes. Although the XBee\LoRa devices communicate in the ISM band with limited payload capability, we have used these to implement security algorithms that are usually agnostic to the physical-layer architecture. All the protocols implemented on these devices can also be applied to 5 G-compatible devices in future.

• All the XBee and LoRa devices are enabled with multi-hop communication to demonstrate the spatial-provenance embedding recovery methods.

Message flow for Spatial-Provenance

• Once the vehicles are registered with the RSU and have completed the neighbour discovery protocol, RSU initiates the broadcast phase, wherein, the dictionary comprising the number of segments and their identities is communicated to all vehicles.

• We highlight that the chosen number of segments is apriori decided in mutual agreement with the vehicles to preserve their privacy.

• As exemplified in  Figure, a source vehicle embeds its spatial-provenance into the Bloom filter of the packet and forwards it to the next vehicle in the path.

•  Subsequently, the next vehicle repeats the process of embedding its spatial-provenance until the packet reaches the RSU.

• To execute these steps, we choose the underlying parameters such as number of hash function for Bloom filter and the Bloom filter size, based on an offline optimization process.

• Furthermore, given that Bloom filters are inherently sparse, i.e., having fewer number of ones than zeros in their data structure, we ask each vehicle to compress its provenance using RAKE compression.

• Consequently, every vehicle that intends to embed its spatial-provenance implements a RAKE decompression algorithm on the reception of a packet.

• Finally, once the packet is received at the RSU, it verifies the location of the vehicles using their identities and the dictionary. This way, every vehicle is localised at the RSU, respecting the privacy constraints of the vehicles.

Highlights of  Testbed

Related Publications

1. M. Bansal, P. Shrivastava and J. Harshan, "A Prototype on the Feasibility of Learning Spatial Provenance in XBee and LoRa Networks,"  in Proc. COMSNETS 2024, Bengaluru, India.

2. M. Bansal, P. Shrivastava and J. Harshan, "On Learning Spatial Provenance in Privacy-Constrained Wireless Networks,"  in Proc. IEEE WCNC 2024, Dubai, United Arab Emirates.