2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014
ABSTRACT The need of sharing fingerprint image data in many emerging applications raises concerns... more ABSTRACT The need of sharing fingerprint image data in many emerging applications raises concerns about the protection of privacy. It has become possible to use automated algorithms for inferring soft biometrics from fingerprint images. Even if we cannot uniquely match the person to an existing fingerprint, revealing their age or gender may lead to undesirable consequences. Our research is focused on de-identifying fingerprint images in order to obfuscate soft biometrics. In this paper, we first discuss a general framework for soft biometrics fingerprint de-identification. We implemented the framework to reduce the risk of successful estimation of gender from fingerprint images using ad-hoc image filtering. We evaluate the proposed approach through experiments using a data set of rolled fingerprints collected at West Virginia University. Results show the proposed method is effective in preventing gender estimation from fingerprint images.
The appeal of including adaptive components in complex computational systems, such as flight cont... more The appeal of including adaptive components in complex computational systems, such as flight control, is in their ability to cope with a changing environment. Continual changes induce uncertainty that limits the applicability of conventional verification and validation (V&V) techniques. In safety-critical applications, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. We present a
... which treat the system as a whole and eliminate valuable component information from the analy... more ... which treat the system as a whole and eliminate valuable component information from the analysis, as discussed in [4]. Component-based software reliability estimation tech-niques can be broadly classified as state based models, path-based models and additive models. ...
ABSTRACT Application domains in which early performance evaluation is needed are becoming more co... more ABSTRACT Application domains in which early performance evaluation is needed are becoming more complex. In addition to traditional measures of complexity due, for example, to the number of components, their interactions, complicated control coordination and schemes, emerging applications may require adaptive response and reconfiguration the impact of externally observable (security) parameters. In this paper we introduce an approach for effective modeling and analysis of performance and security tradeoffs. The approach identifies a suitable allocation of resources that meet performance requirements, while maximizing measurable security effects. We demonstrate this approach through the analysis of performance sensitivity of a Border Inspection Management System (BIMS) with changing security mechanisms (e.g. biometric system parameters for passenger identification). The final result is a model-based approach that allows us to take decisions about BIMS performance and security mechanisms on the basis of rates of traveler arrivals and traveler identification security guarantees. We describe the experience gained when applying this approach to daily flight arrival schedule of a real airport.
ABSTRACT The appeal of including biologically inspired soft computing systems such as neural netw... more ABSTRACT The appeal of including biologically inspired soft computing systems such as neural networks in complex computational systems is in their ability to cope with a changing environment. Unfortunately, continual changes induce uncertainty that limits the applicability of conventional verification and validation (V&V) techniques to assure the reliable performance of such systems. At the system input layer, novel data may cause unstable learning behavior, which may contribute to system failures. Thus, the changes at the input layer must be observed, diagnosed, accommodated and well understood prior to system deployment. Moreover, at the system output layer, the uncertainties/novelties existing in the neural network predictions also need to be well analyzed and detected during system operation. Our research tackles the novelty detection problem at both layers using two different methods. We use a statistical learning tool, support vector data description (SVDD), as a one-class classifier to examine the data entering the adaptive component and detect unforeseen patterns that may cause abrupt system functionality changes. At the output layer, we define a reliability-like measure, the validity index. The validity index reflects the degree of novelty associated with each output and thus can be used to perform system validity checks. Simulations demonstrate that both techniques effectively detect unusual events and provide validation inferences in a near-real time manner.
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2005
ABSTRACT This paper describes a methodology for generating indicators of performance for the dyna... more ABSTRACT This paper describes a methodology for generating indicators of performance for the dynamic cell structures neural network, a type of growing self-organizing map. The performance indicators are based on the learning architecture of the neural network and are validated using correlation measures of Murphy's rule. Time estimates for neural network convergence are generated based on the current data conditions and the confidence in the neural network, which is provided by the performance indicators. Analytical and experimental results are presented for the dynamic cell structures neural network during its training from the Carnegie Mellon University two-spirals benchmark data.
2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014
ABSTRACT The need of sharing fingerprint image data in many emerging applications raises concerns... more ABSTRACT The need of sharing fingerprint image data in many emerging applications raises concerns about the protection of privacy. It has become possible to use automated algorithms for inferring soft biometrics from fingerprint images. Even if we cannot uniquely match the person to an existing fingerprint, revealing their age or gender may lead to undesirable consequences. Our research is focused on de-identifying fingerprint images in order to obfuscate soft biometrics. In this paper, we first discuss a general framework for soft biometrics fingerprint de-identification. We implemented the framework to reduce the risk of successful estimation of gender from fingerprint images using ad-hoc image filtering. We evaluate the proposed approach through experiments using a data set of rolled fingerprints collected at West Virginia University. Results show the proposed method is effective in preventing gender estimation from fingerprint images.
The appeal of including adaptive components in complex computational systems, such as flight cont... more The appeal of including adaptive components in complex computational systems, such as flight control, is in their ability to cope with a changing environment. Continual changes induce uncertainty that limits the applicability of conventional verification and validation (V&V) techniques. In safety-critical applications, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. We present a
... which treat the system as a whole and eliminate valuable component information from the analy... more ... which treat the system as a whole and eliminate valuable component information from the analysis, as discussed in [4]. Component-based software reliability estimation tech-niques can be broadly classified as state based models, path-based models and additive models. ...
ABSTRACT Application domains in which early performance evaluation is needed are becoming more co... more ABSTRACT Application domains in which early performance evaluation is needed are becoming more complex. In addition to traditional measures of complexity due, for example, to the number of components, their interactions, complicated control coordination and schemes, emerging applications may require adaptive response and reconfiguration the impact of externally observable (security) parameters. In this paper we introduce an approach for effective modeling and analysis of performance and security tradeoffs. The approach identifies a suitable allocation of resources that meet performance requirements, while maximizing measurable security effects. We demonstrate this approach through the analysis of performance sensitivity of a Border Inspection Management System (BIMS) with changing security mechanisms (e.g. biometric system parameters for passenger identification). The final result is a model-based approach that allows us to take decisions about BIMS performance and security mechanisms on the basis of rates of traveler arrivals and traveler identification security guarantees. We describe the experience gained when applying this approach to daily flight arrival schedule of a real airport.
ABSTRACT The appeal of including biologically inspired soft computing systems such as neural netw... more ABSTRACT The appeal of including biologically inspired soft computing systems such as neural networks in complex computational systems is in their ability to cope with a changing environment. Unfortunately, continual changes induce uncertainty that limits the applicability of conventional verification and validation (V&V) techniques to assure the reliable performance of such systems. At the system input layer, novel data may cause unstable learning behavior, which may contribute to system failures. Thus, the changes at the input layer must be observed, diagnosed, accommodated and well understood prior to system deployment. Moreover, at the system output layer, the uncertainties/novelties existing in the neural network predictions also need to be well analyzed and detected during system operation. Our research tackles the novelty detection problem at both layers using two different methods. We use a statistical learning tool, support vector data description (SVDD), as a one-class classifier to examine the data entering the adaptive component and detect unforeseen patterns that may cause abrupt system functionality changes. At the output layer, we define a reliability-like measure, the validity index. The validity index reflects the degree of novelty associated with each output and thus can be used to perform system validity checks. Simulations demonstrate that both techniques effectively detect unusual events and provide validation inferences in a near-real time manner.
Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2005
ABSTRACT This paper describes a methodology for generating indicators of performance for the dyna... more ABSTRACT This paper describes a methodology for generating indicators of performance for the dynamic cell structures neural network, a type of growing self-organizing map. The performance indicators are based on the learning architecture of the neural network and are validated using correlation measures of Murphy's rule. Time estimates for neural network convergence are generated based on the current data conditions and the confidence in the neural network, which is provided by the performance indicators. Analytical and experimental results are presented for the dynamic cell structures neural network during its training from the Carnegie Mellon University two-spirals benchmark data.
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Papers by B. Cukic