18 research outputs found
On the Design and Analysis of Secure Inference Networks
Parallel-topology inference networks consist of spatially-distributed sensing agents that collect and transmit observations to a central node called the fusion center (FC), so that a global inference is made regarding the phenomenon-of-interest (PoI). In this dissertation, we address two types of statistical inference, namely binary-hypothesis testing and scalar parameter estimation in parallel-topology inference networks. We address three different types of security threats in parallel-topology inference networks, namely Eavesdropping (Data-Confidentiality), Byzantine (Data-Integrity) or Jamming (Data-Availability) attacks. In an attempt to alleviate information leakage to the eavesdropper, we present optimal/near-optimal binary quantizers under two different frameworks, namely differential secrecy where the difference in performances between the FC and Eve is maximized, and constrained secrecy where FC’s performance is maximized in the presence of tolerable secrecy constraints. We also propose near-optimal transmit diversity mechanisms at the sensing agents in detection networks in the presence of tolerable secrecy constraints. In the context of distributed inference networks with M-ary quantized sensing data, we propose a novel Byzantine attack model and find optimal attack strategies that minimize KL Divergence at the FC in the presence of both ideal and non-ideal channels. Furthermore, we also propose a novel deviation-based reputation scheme to detect Byzantine nodes in a distributed inference network. Finally, we investigate optimal jamming attacks in detection networks where the jammer distributes its power across the sensing and the communication channels. We also model the interaction between the jammer and a centralized detection network as a complete information zero-sum game. We find closed-form expressions for pure-strategy Nash equilibria and show that both the players converge to these equilibria in a repeated game. Finally, we show that the jammer finds no incentive to employ pure-strategy equilibria, and causes greater impact on the network performance by employing mixed strategies
Secure distributed detection in wireless sensor networks via encryption of sensor decisions
We consider the problem of binary hypothesis testing using a distributed wireless sensor network. Identical binary quantizers are used on the sensor\u27s observations and the outputs are encrypted using a probabilistic cipher. The third party (enemy) fusion centers are unaware of the presence of the probabilistic encipher. We find the optimal (minimum-probability-of-error) fusion rule for the ally (friendly) fusion center subject to a lower bound on the the probability of error for the third-party fusion centers. To obtain the minimum probability of error, we first prove the quasi-convexity of error probability with respect to the sensor\u27s threshold for a given cipher and show the existence of a unique positive minimum for error probability of the ally fusion center. The threshold corresponding to the minimum error-probability is evaluated numerically and the appropriate cipher that deteriorates the performance of the third-party fusion center below the required limits is obtained. Our results show that, by adjusting the sensor threshold and the encryption parameters, it is possible to achieve acceptable performance for the ally fusion center while causing significant degradation to the performance of the third party fusion center
On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization
Revealed preference theory studies the possibility of modeling an agent's
revealed preferences and the construction of a consistent utility function.
However, modeling agent's choices over preference orderings is not always
practical and demands strong assumptions on human rationality and
data-acquisition abilities. Therefore, we propose a simple generative choice
model where agents are assumed to generate the choice probabilities based on
latent factor matrices that capture their choice evaluation across multiple
attributes. Since the multi-attribute evaluation is typically hidden within the
agent's psyche, we consider a signaling mechanism where agents are provided
with choice information through private signals, so that the agent's choices
provide more insight about his/her latent evaluation across multiple
attributes. We estimate the choice model via a novel multi-stage matrix
factorization algorithm that minimizes the average deviation of the factor
estimates from choice data. Simulation results are presented to validate the
estimation performance of our proposed algorithm.Comment: 6 pages, 2 figures, to be presented at CISS conferenc
Towards Inclusive Fairness Evaluation via Eliciting Disagreement Feedback from Non-Expert Stakeholders
Traditional algorithmic fairness notions rely on label feedback, which can
only be elicited from expert critics. However, in most practical applications,
several non-expert stakeholders also play a major role in the system and can
have distinctive opinions about the decision making philosophy. For example, in
kidney placement programs, transplant surgeons are very wary about accepting
kidney offers for black patients due to genetic reasons. However, non-expert
stakeholders in kidney placement programs (e.g. patients, donors and their
family members) may misinterpret such decisions from the perspective of social
discrimination. This paper evaluates group fairness notions from the viewpoint
of non-expert stakeholders, who can only provide binary
\emph{agreement/disagreement feedback} regarding the decision in context.
Specifically, two types of group fairness notions have been identified: (i)
\emph{definite notions} (e.g. calibration), which can be evaluated exactly
using disagreement feedback, and (ii) \emph{indefinite notions} (e.g. equal
opportunity) which suffer from uncertainty due to lack of label feedback. In
the case of indefinite notions, bounds are presented based on disagreement
rates, and an estimate is constructed based on established bounds. The efficacy
of all our findings are validated empirically on real human feedback dataset
Strategic Communication Between Prospect Theoretic Agents over a Gaussian Test Channel
In this paper, we model a Stackelberg game in a simple Gaussian test channel
where a human transmitter (leader) communicates a source message to a human
receiver (follower). We model human decision making using prospect theory
models proposed for continuous decision spaces. Assuming that the value
function is the squared distortion at both the transmitter and the receiver, we
analyze the effects of the weight functions at both the transmitter and the
receiver on optimal communication strategies, namely encoding at the
transmitter and decoding at the receiver, in the Stackelberg sense. We show
that the optimal strategies for the behavioral agents in the Stackelberg sense
are identical to those designed for unbiased agents. At the same time, we also
show that the prospect-theoretic distortions at both the transmitter and the
receiver are both larger than the expected distortion, thus making behavioral
agents less contended than unbiased agents. Consequently, the presence of
cognitive biases increases the need for transmission power in order to achieve
a given distortion at both transmitter and receiver.Comment: 6 pages, 3 figures, Accepted to MILCOM-2017, Corrections made in the
new versio
Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram
Spiking neural networks (SNNs) are receiving increased attention as a means
to develop "biologically plausible" machine learning models. These networks
mimic synaptic connections in the human brain and produce spike trains, which
can be approximated by binary values, precluding high computational cost with
floating-point arithmetic circuits. Recently, the addition of convolutional
layers to combine the feature extraction power of convolutional networks with
the computational efficiency of SNNs has been introduced. In this paper, the
feasibility of using a convolutional spiking neural network (CSNN) as a
classifier to detect anticipatory slow cortical potentials related to braking
intention in human participants using an electroencephalogram (EEG) was
studied. The EEG data was collected during an experiment wherein participants
operated a remote controlled vehicle on a testbed designed to simulate an urban
environment. Participants were alerted to an incoming braking event via an
audio countdown to elicit anticipatory potentials that were then measured using
an EEG. The CSNN's performance was compared to a standard convolutional neural
network (CNN) and three graph neural networks (GNNs) via 10-fold
cross-validation. The results showed that the CSNN outperformed the other
neural networks.Comment: 14 pages, 6 figures, Scientific Reports submissio