Traffic De Correlation Techniques for Countering a Global Eavesdropper in
Traffic De Correlation. We address the problem of preventing inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of packets transmitted, interpacket times, and traffic directionality, to infer the location of the event, its occurrence time, and the sink location.
- The proposed Traffic De Correlation Techniques system reduces the communication and delay overheads by limiting the injected bogus traffic.
- The proposed system reduces the forwarding delay
- We compare privacy and overhead of our techniques to prior art and show the savings achieved.
- First, eavesdroppers are passive devices that are hard to detect.
- Second, the availability of low-cost commodity radio hardware makes it inexpensive to deploy a large number of eavesdroppers.
- Third, even if encryption is applied to conceal the packet payload, some fields in the packet headers still need to be transmitted in the clear for correct protocol operation (e.g., PHY-layer headers used for frame detection, synchronization, etc.). These unencrypted fields facilitate accurate estimation of transmission attributes.
- High communication overhead and increased end-to-end delay for reporting events.
Traffic De Correlation techniques for countering a global eavesdropper in WSNs. Under a global eavesdropper, we addressed the issue of contextual information privacy in WSNs.
We presented a general method of traffic analysis to collectively process packet interception times and eavesdropper locations at a fusion center. To mitigate global eavesdropping, we have proposed methods for normalizing traffic that regulate the traffic patterns of a sub-set of sensors that make up MCDSs.