A security index for biometric systems is essential because biometrics have been widely adopted as a secure authentication component in critical systems. Most of bio-metric systems secured by template protection schemes are based on binary templates. To adopt popular template protection schemes such as fuzzy commitment and fuzzy extractor that can be applied on binary templates only, non-binary templates (e.g., real-valued, point-set based) need to be converted to binary. However, existing security measurements for binary template based biometric systems either cannot reflect the actual attack difficulties or are too computationally expensive to be practical. This paper presents an acceleration of the guessing entropy which reflects the expected number of guessing trials in attacking the binary template based biometric systems. The acceleration benefits from computation reuse and pruning. Experimental results on two datasets show that the acceleration has more than 6x, 20x, and 200x speed up without losing the estimation accuracy in different system settings.

Biometric verification systems are designed to accept multiple similar biometric measurements per user due to inherent intrauser variations in the biometric data. This is important to preserve reasonable acceptance rate of genuine queries and the overall feasibility of the recognition system. However, such acceptance of multiple similar measurements decreases the imposter's difficulty of obtaining a system-acceptable measurement, thus resulting in a degraded security level. This deteriorated security needs to be measurable to provide truthful security assurance to the users. Entropy is a standard measure of security. However, the entropy formula is applicable only when there is a single acceptable possibility. In this paper, we develop an entropy-measuring model for biometric systems that accepts multiple similar measurements per user. Based on the idea of guessing entropy, the proposed model quantifies biometric system security in terms of adversarial guessing effort for two practical attacks. Excellent agreement between analytic and experimental simulation-based measurement results on a synthetic and a benchmark face dataset justify the correctness of our model and thus the feasibility of the proposed entropy-measuring approach.

The ISO/IEC 19794-2-compliant fingerprint minutiae template is an unordered and variable-sized point set data. Such a characteristic leads to a restriction for the applications that can only operate on fixed-length binary data, such as cryptographic applications and certain biometric cryptosystems (e.g., fuzzy commitment). In this paper, we propose a generic point-to-string conversion framework for fingerprint minutia based on kernel learning methods to generate discriminative fixed length binary strings, which enables rapid matching. The proposed framework consists of four stages: (1) minutiae descriptor extraction; (2) a kernel transformation method that is composed of kernel principal component analysis or kernelized locality-sensitive hashing for fixed length vector generation; (3) a dynamic feature binarization; and (4) matching. The promising experimental results on six datasets from fingerprint verification competition (FVC)2002 and FVC2004 justify the feasibility of the proposed framework in terms of matching accuracy, efficiency, and template randomness.

For a large-scale software system, especially an online service system, when a performance issue occurs, it is desirable to check whether this issue has occurred before. If there are past similar issues, a known remedy could be applied. Otherwise, a new troubleshooting process may have to be initiated. The symptom of a performance issue can be characterized by a set of metrics. Due to the sophisticated nature of software systems, manual diagnosis of performance issues based on metric data is typically expensive and laborious. In this paper, we propose a Hidden Markov Random Field (HMRF) based approach to automatic identification of recurrent and unknown performance issues. We formulate the problem of issue identification as a HMRF-based clustering problem. Our approach incorporates the learning of metric discretization thresholds and the optimization of issue clustering. Based on the learned thresholds and cluster centroids, we can achieve accurate identification of recurrent issues and unknown issues. Experimental evaluations on an open benchmark and a large-scale industrial production system show that our approach is effective and outperforms the related state-of-the-art approaches.

Separability in a code is crucial in guaranteeing a decent Hamming-distance separation among the codewords. In multibit biometric discretization where a code is used for quantization-intervals labeling, separability is necessary for preserving distance dissimilarity when feature components are mapped from a discrete space to a Hamming space. In this paper, we examine separability of Binary Reflected Gray Code (BRGC) encoding and reveal its inadequacy in tackling interclass variation during the discrete-to-binary mapping, leading to a tradeoff between classification performance and entropy of binary output. To overcome this drawback, we put forward two encoding schemes exhibiting full-ideal and near-ideal separability capabilities, known as Linearly Separable Subcode (LSSC) and Partially Linearly Separable Subcode (PLSSC), respectively. These encoding schemes convert the conventional entropy-performance tradeoff into an entropy-redundancy tradeoff in the increase of code length. Extensive experimental results vindicate the superiority of our schemes over the existing encoding schemes in discretization performance. This opens up possibilities of achieving much greater classification performance with high output entropy.

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

Biometric discretization derives a binary string for each user based on an ordered set of real-valued biometric features. The false acceptance rate (FAR) and the false rejection rate (FRR) of a binary biometric-based system significantly relies on a Hamming distance threshold which decides whether the errors in the query bit string will be rectified with reference to the template bit string. Kelkboom have recently modeled a basic framework to estimate the FAR and the FRR of one-bit biometric discretization. However, as the demand of a bit string with higher entropy (informative length) rises, single-bit discretization is getting less useful today due to its incapability of producing bit string that is longer than the total feature dimensions being extracted, thus causing Kelkboom's model to be of restricted use. In this paper, we extend the analytical framework to multibit discretization for estimating the performance and the decision threshold for achieving a specified FAR/FRR based on equal-probable quantization and linearly separable subcode encoding. Promising estimation results on a synthetic data set with independent feature components and Gaussian measurements vindicate the analytical expressions of our framework. However, for experiments on two popular face data sets, deviation in estimation results were obtained mainly due to the mismatch of independency assumption of our framework. We hence fit the analytical probability mass functions (pmfs) to the experimental pmfs through estimating the mean and the variance parameters from the difference between the corresponding analytical and experimental curves to alleviate such estimation inaccuracies on these data sets.

Biometrie discretization converts extracted biométrie features into a binary string via a process of segmenting every one-dimensional feature space into possibly distinct multiple intervals and encoding every interval-captured feature element correspondingly. Eventually, the individual binary output of every feature element is concatenated into a binary string. To the best of our knowledge, Detection Rate Optimized Bit Allocation (DROBA) scheme is currently the most effective biométrie quantization scheme, offering its capability in assigning bits dynamically for each user-specific feature. However, we discover that DROBA suffers from potential discriminative feature miss-detections and under-quantized conditions. This paper highlights such drawbacks and improves upon DROBA by incorporating a dynamic searching method to efficiently recapture such miss-detected features. Experimental results illustrating significant improvements in classification accuracy justify the practicality of our approach.

Key management is deemed as the fundamental essential part of any secure communication. A secure sensor network communication protocol relies on the substantial secure, robust and efficient key management system. We put forward a new hybrid group key management scheme for hierarchical self organizing wireless sensor network architecture. By using this approach, multi-level security can be achieved to secure groups of sensors at different levels. We place the cryptographic burden where the resources are less constrained, at the forwarding nodes and the access points. In our proposed scheme, access points and forwarding nodes initially perform a key agreement protocol and each sensor node in a cluster later on establishes a group key with the forwarding node using a key transport scheme dynamically after deployment.