Temporal Differential Privacy in Wireless Sensor Networks Abstract   Sustaining the temporal privacy of an event in a wireless sensor network (WSN) is a critical task. From an adversarial point of view, the privacy of an event of interest can be breached by analyzing the traffic which emanates from a particular region. In this study, we present a differentially private technique for achieving temporal privacy by hiding the reporting traffic trace in the midst of other traffic traces. We achieve this objective by selectively delaying traffic traces at the nodes which are present in the routing paths of the messages to the sink. This mechanism prevents an adversary from gaining additional information about the time of occurrence of an event subjected to the presence or absence of traffic corresponding to a particular node. In this work, a packet forwarding path corresponding to a reporting event is modeled as an open queuing network. The temporal privacy associated with the event is measured by the jitter of the network. In our simulation, the minimum jitter for a basic FCFS and prioritized queuing network was estimated to be 0.45ms and 0.97ms respectively, whereas the jitter for the same networks after enforcing the privacy preservation mechanism was approximated to be 436.15ms and 503.42ms respectively. These results demonstrate the effects of differential privacy for concealing temporal information about the traffic corresponding to any node.

Probabilistic routing in wireless networks with privacy guarantees Abstract   We consider the sourcexe2x80x93destination location privacy problem for routing in wireless networks. Previous routing schemes mainly provided privacy protection by minimizing the average detection probability of traffic analysis attempts. However, they do not seek to provide strict privacy guarantees of the vulnerable sourcexe2x80x93destination pairs, which could still be relatively easy to identify. To address this gap in the literature, we propose the     (  k  ,  xcfxb5  )    anonymity property for routing in wireless networks with privacy guarantees. We consider a Bayesian maximumaposteriori (MAP) inferencebased adversary and design a probabilistic routing scheme that uses a statistical decisionmaking framework to compute the minimumcost     (  k  ,  xcfxb5  )    anonymous paths. A routing scheme is     (  k  ,  xcfxb5  )    anonymous if there are    k    or more distinct sourcexe2x80x93destination pairs within an    xcfxb5   tolerance of the MAP probability. We compare our approach against a baseline routing scheme that minimizes the average detection probability of the adversary, and our simulation results show that our approach provides significantly better     (  k  ,  xcfxb5  )    anonymity privacy guarantees while achieving comparable average adversarial detection probability. We also studied how the adversaryxe2x80x99s prior beliefs affect its detection probability and Bayes risk.