Francis Minhthang Bui

Biometric methods for secure communications in body sensor networks: resource-efficient key management and signal-level data scrambling

As electronic communications become more prevalent, mobile and universal, the threats of data compromises also accordingly loom larger. In the context of a body sensor network (BSN), which permits pervasive monitoring of potentially sensitive medical data, security and privacy concerns are particularly important. It is a challenge to implement traditional security infrastructures in these types of lightweight networks since they are by design limited in both computational and communication resources. A key enabling technology for secure communications in BSNs has emerged to be biometrics. In this work, we present two complementary approaches which exploit physiological signals to address security issues: (1) a resource-efficient key management system for generating and distributing cryptographic keys to constituent sensors in a BSN; (2) a novel data scrambling method, based on interpolation and random sampling, that is envisioned as a potential alternative to conventional symmetric encryption algorithms for certain types of data. The former targets the resource constraints in BSNs, while the latter addresses the fuzzy variability of biometric signals, which has largely precluded the direct application of conventional encryption. Using electrocardiogram (ECG) signals as biometrics, the resulting computer simulations demonstrate the feasibility and efficacy of these methods for delivering secure communications in BSNs.

Automated Bleeding Detection in Capsule Endoscopy Videos Using Statistical Features and Region Growing

Wireless Capsule Endoscopy (WCE) is a technology in the field of endoscopic imaging which facilitates direct visualization of the entire small intestine. Many algorithms are being developed to automatically identify clinically important frames in WCE videos. This paper presents a supervised method for automated detection of bleeding regions present in WCE frames or images. The proposed method characterizes the image regions by using statistical features derived from the first order histogram probability of the three planes of RGB color space. Despite being inconsistent and tiresome, manual selection of regions has been a popular technique for creating training data in the studies of capsule endoscopic images. We propose a semi-automatic region-annotation algorithm for creating training data efficiently. All possible combinations of different features are exhaustively analyzed to find the optimum feature set with the best performance. During operation, regions from images are obtained by applying a segmentation method. Finally, a trained neural network recognizes the patterns of the data arising from bleeding and non-bleeding regions.

Fuzzy Key Binding Strategies Based on Quantization Index Modulation (QIM) for Biometric Encryption (BE) Applications

Biometric encryption (BE) has recently been identified as a promising paradigm to deliver security and privacy, with unique technical merits and encouraging social implications. An integral component in BE is a key binding method, which is the process of securely combining a signal, containing sensitive information to be protected (i.e., the key), with another signal derived from physiological features (i.e., the biometric). A challenge to this approach is the high degree of noise and variability present in physiological signals. As such, fuzzy methods are needed to enable proper operations, with adequate performance results in terms of false acceptance rate and false rejection rate. In this work, the focus will be on a class of fuzzy key binding methods based on dirty paper coding known as quantization index modulation. While the methods presented are applicable to a wide range of biometric modalities, the face biometric is selected for illustrative purposes, in evaluating the QIM-based solutions for BE systems. Performance evaluation of the investigated methods is reported using data from the CMU PIE face database.

Face recognition with biometric encryption for privacy-enhancing self-exclusion

Face recognition has been employed in various security-related applications such as surveillance, mugshot identification, e-passport, and access control. Despite its recent advancements, privacy concern is one of several issues preventing its wider deployment. In this paper, we address the privacy concern for a self-exclusion scenario of face recognition, through combining face recognition with a simple biometric encryption scheme called helper data system. The combined system is described in detail with focus on the key binding procedure. Experiments are carried out on the CMU PIE face database. The experimental results demonstrate that in the proposed system, the biometric encryption module tends to significantly reduce the false acceptance rate while increasing the false rejection rate.

Medical biometrics: The perils of ignoring time dependency

The electrocardiogram (ECG) is a medical signal that has lately drawn interest from the biometrics community, and has been shown to have significantly discriminative characteristics in a population. This paper brings to light the particular challenges of electrocardiogram recognition to advocate that time dependency is a controversial point. In contrast to traditional biometrics, ECG allows for continuous authentication and consequently expands the range of applications. However, time varying biometrics put on the line the recognition accuracy due to increased intra subject variability. This paper suggests a novel framework for bypassing this inadequacy. A template update methodology is proposed and demonstrated to boost the recognition performance over 2 hour recordings of 10 subjects.

Medical biometrics in mobile health monitoring

This work investigates the feasibility of ECG-based identity management in mobile health monitoring applications. A body area network that operates in conjunction with ECG biometric recognition is explored for mobile monitoring of patients, rescuers, pilots, soldiers, or field agents in general. Among the major challenges of this technology is the stability of the signals over the monitoring duration. Time dependency is responsible for ECG destabilization, which becomes a significant issue for reliable monitoring. We propose a novel framework that addresses this inadequacy, by updating a gallery template when feature matching is compromised. In addition, strategies for tackling privacy issues in medical data management are proposed. A protocol level solution is discussed, to deal with the ethical issues of this technology. An automatic way of aggregating and managing personal information is presented, designated to operate on the basis of anonymity. The experimental performance measured over long-ECG recordings demonstrates promising results. Copyright

A Game-Theoretic Framework for Robust Optimal Intrusion Detection in Wireless Sensor Networks

A robust optimization model is considered for nonzero-sum discounted stochastic games with incomplete information in order to formally formulate and analyze the intrusion detection problem in wireless sensor networks (WSNs). Security requirements of WSNs are taken into account to characterize the game parameters and model the player objectives. To generalize the problem, the game data are assumed not to be fully known to the players, who take a robust optimization approach to address this data uncertainty. For assessing the validity and effectiveness of the framework, illustrative instances of the developed game model are generated. Equilibrium analysis reveals how the conflicting objectives of the intruder and intrusion detection system compel them to adopt different conservative stances toward data uncertainty. It is also shown, by numerical results, that the robust approach in the presence of uncertainty reduces the sensitivity of the solution with respect to data perturbations, and thus improves design stability.

Bleeding detection in wireless capsule endoscopy based on color features from histogram probability

This paper presents a novel technique for detecting bleeding regions in capsule endoscopy images. The proposed algorithm extracts color features from image-regions by calculating mean, standard deviation, skew and energy from the first order histogram of the RGB planes separately. Through the use of RGB color space, three times more number of features can be obtained than while using a grayscale image. Such color features have been used in content based retrieval system in pathology images. However, in spite of simplicity and ease of calculation, these features have not yet been studied in the classification of bleeding and non-bleeding regions in capsule endoscopic images. This paper studies the feasibility of using these features by assessing all possible feature subsets through the use of classification accuracy. The proposed algorithm could obtain classification accuracy up to 89%.

A Biometric Encryption System for the Self-Exclusion Scenario of Face Recognition

This paper presents a biometric encryption system that addresses the privacy concern in the deployment of the face recognition technology in real-world systems. In particular, we focus on a self-exclusion scenario (a special application of watch-list) of face recognition and propose a novel design of a biometric encryption system deployed with a face recognition system under constrained conditions. From a system perspective, we investigate issues ranging from image preprocessing, feature extraction, to cryptography, error-correcting coding/decoding, key binding, and bit allocation. In simulation studies, the proposed biometric encryption system is tested on the CMU PIE face database. An important observation from the simulation results is that in the proposed system, the biometric encryption module tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate.

Optimal Relay Selection and Power Control With Quality-of-Service Provisioning in Wireless Body Area Networks

A game-theoretic approach is proposed to investigate the problem of relay selection and power control with quality of service constraints in multiple-access wireless body area networks (WBANs). Each sensor node seeks a strategy that ensures the optimal energy efficiency and, at the same time, provides a guaranteed upper bound on the end-to-end packet delay and jitter. The existence of Nash equilibrium for the proposed non-cooperative game is proved, the Nash power control solution is analytically calculated, and a distributed algorithm is provided that converges to a Nash relay selection solution. The game theoretic analysis is then employed in an IEEE 802.15.6-based WBAN to gauge the validity and effectiveness of the proposed framework. Performance behaviors in terms of energy efficiency and end-to-end delay and jitter are examined for various scenarios. Results demonstrate the merits of the proposed framework, particularly for moving WBANs under severe fading conditions.