Sungju Lee

Automatic alignment of fingerprint features for fuzzy fingerprint vault

Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications. This is also true for new authentication architectures known as crypto-biometric systems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems. This construct aims to secure critical data(e.g., secret encryption key) with the fingerprint data in a way that only the authorized user can access the secret by providing the valid fingerprint, and some implementations results for fingerprint have been reported. However, all the previous results assumed that fingerprint features were pre-aligned, and automatic alignment in the fuzzy vault domain is a challenging issue. In this paper, we perform the automatic alignment of fingerprint features by using the geometric hashing technique which has been used for model-based object recognition applications. Based on the preliminary experimental results, we confirm that the proposed approach can align fingerprint features automatically in the domain of the fuzzy vault and can be integrated with any fuzzy fingerprint vault systems.

Protecting Secret Keys with Fuzzy Fingerprint Vault Based on a 3D Geometric Hash Table

Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications such as home networks. This is also true for new authentication architectures known as crypto-biometricsystems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. Recently, a cryptographic construct, called fuzzy vault, has been proposed for crypto-biometric systems. In this paper, we propose an approach to provide both the automatic alignment of fingerprint data and higher security by using a 3D geometric hash table. Based on the experimental results, we confirm that the proposed approach of using the 3D geometric hash table with the idea of the fuzzy vaultcan perform the fingerprint verification securely even with one thousand chaff data included.

Fingerprint template protection using fuzzy vault

Biometric-based authentication can provide strong security guarantee about the identity of users. However, security of biometric data is particularly important as the compromise of the data will be permanent. To protect the biometric data, we need to store it in a non-invertible transformed version. Thus, even if the transformed version is compromised, the actual biometric data remains safe. In this paper, we propose an approach to protect fingerprint templates by using the idea of the fuzzy vault. Fuzzy vault is a recently developed cryptographic construct to secure critical data with the fingerprint data in a way that only the authorized user can access the secret by providing the valid fingerprint. We modify the fuzzy vault to protect fingerprint templates and to perform fingerprint verification with the protected template at the same time. This is challenging because the fingerprint verification is performed in the domain of the protected form. Based on the experimental results, we confirm that the proposed approach can perform the fingerprint verification with the protected template.

Fast Video Encryption Using the H.264 Error Propagation Property for Smart Mobile Devices

In transmitting video data securely over Video Sensor Networks (VSNs), since mobile handheld devices have limited resources in terms of processor clock speed and battery size, it is necessary to develop an efficient method to encrypt video data to meet the increasing demand for secure connections. Selective encryption methods can reduce the amount of computation needed while satisfying high-level security requirements. This is achieved by selecting an important part of the video data and encrypting it. In this paper, to ensure format compliance and security, we propose a special encryption method for H.264, which encrypts only the DC/ACs of I-macroblocks and the motion vectors of P-macroblocks. In particular, the proposed new selective encryption method exploits the error propagation property in an H.264 decoder and improves the collective performance by analyzing the tradeoff between the visual security level and the processing speed compared to typical selective encryption methods (i.e., I-frame, P-frame encryption, and combined I-/P-frame encryption). Experimental results show that the proposed method can significantly reduce the encryption workload without any significant degradation of visual security.

Slice-level selective encryption for protecting video data

SECMPEG selectively encrypts data to protect MPEG video streams. However, this scheme is inefficient because it encrypts the whole I-frames whose size is relatively larger than B/P-frames. In this paper, we propose a more efficient selective encryption approach which exploits the error propagation property in MPEG2 standard. Our experimental results show that the proposed approach can reduce the execution time of SECMPEG by a factor of 32 without degradation of the security.

A fast algorithm for polynomial reconstruction of fuzzy fingerprint vault

Biometric based authentication can provide strong security guarantee about the identity of users. However, security of biometric data is particularly important as compromise of the data will be permanent. Cancelable biometrics stores a non - invertible transformed version of the biometric data. Thus, even if the storage is compromised, the biometric data remains safe. Cancelable biometrics also provide a higher level of privacy by allowing many templates for the same biometric data and hence non-linkability of users data stored in different databases. In this paper, we proposed the fast polynomial reconstruction algorithm for fuzzy fingerprint vault. The proposed method needs (k+1) real points to reconstruct the polynomial of degree (k-1). It enhances the speed, however, by times according to the degree of polynomial compared with the exhaust search.

Configurable fuzzy fingerprint vault for Match-on-Card system

The fuzzy fingerprint vault has been proposed for protecting the fingerprint template by adding a number of redundant features. Recently, a correlation attack that finds the real features using multiple fingerprint vaults has been introduced by several researches and should be solved for practical implementations. To avoid this correlation attack, we propose the Match-on-Card system based on the fuzzy fingerprint vault where the fingerprint matching is performed by an in-card processor, not an external card reader. Since the vault information is not released from the smart card, it is difficult for an attacker to take multiple fingerprint vaults. However, because of the limited resources (i.e., processing power and memory space) of the smart card, integrating fuzzy fingerprint vault into it is still an open challenge. To overcome this limitation, we propose the configurable fuzzy fingerprint vault based on a geometric hashing. Experimental results show that our approach can obtain much less memory requirements (i.e., 50 KB) and faster execution time (i.e., 1.5 sec) than the previous ones (i.e., 277 KB, 6.6 sec) without a significant degradation of the verification accuracy.

Secret Distribution for Secure Fingerprint Verification

Recently, in the smart-card based authentication system, there is an increasing trend of using fingerprint for the card holder verification, instead of passwords. However, the security of the fingerprint data is particularly important as the possible compromise of the data will be permanent. In this paper, we propose an approach for secure fingerprint verification by distributing both the secret and the computation based on the fuzzy vault (a cryptographic construct which has been proposed for crypto-biometric systems). That is, a user fingerprint template which is applied to the fuzzy vault is divided into two parts, and each part is stored into a smart-card and a server, respectively. At distributing the fingerprint template, we consider both the security level and the verification accuracy. Then, the geometric hashing technique is applied to solve the fingerprint alignment problem, and this computation is also distributed over the combination of the smart-card and the server in the form of the challenge-response. Finally, the polynomial can be reconstructed from the accumulated real points from both the smart-card and the server. Based on the experimental results, we confirm that our proposed approach can perform the fuzzy vault-based fingerprint verification more securely on a combination of a smart-card and a server without significant degradation of the verification accuracy.

Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment

A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the cloud server for a comparison of results. In addition, we present a statistical analysis and cloud-based resource allocation method for a heterogeneous platform environment by performing a data and information analysis with consideration of the application workload and the server capacity, and subsequently propose a service prediction model using a polynomial regression model. In particular, our aim is to provide stable service in a given large-scale enterprise cloud computing environment. The virtual machines (VMs) for cloud-based services are assigned to each server with a special methodology to satisfy the uniform utilization distribution model. It is also implemented between users and the platform, which is a main idea of our cloud computing system. Based on the experimental results, we confirm that our prediction model can provide sufficient resources for statistical services to large-scale users while satisfying the uniform utilization distribution.

CPU-GPU hybrid computing for feature extraction from video stream

In this paper, we propose a way to distribute the video analytics workload into both the CPU and GPU, with a performance prediction model including characteristics of feature extraction from the video stream data. That is, we estimate the total execution time of a CPU-GPU hybrid computing system with the performance prediction model, and determine the optimal workload ratio and how to use the CPU cores for the given workload. Based on experimental results, we confirm that our proposed method can improve the speedups of three typical workload distributions: CPU-only, GPU-only, or CPU-GPU hybrid computing with a 50:50 workload ratio.