Yagiz Sutcu

Protecting Biometric Templates With Sketch: Theory and Practice

Secure storage of biometric templates has become an increasingly important issue in biometric authentication systems. We study how secure sketch, a recently proposed error-tolerant cryptographic primitive, can be applied to protect the templates. We identify several practical issues that are not addressed in the existing theoretical framework, and show the subtleties in evaluating the security of practical systems. We propose a general framework to design and analyze a secure sketch for biometric templates, and give a concrete construction for face biometrics as an example. We show that theoretical bounds have their limitations in practical schemes, and the exact security of the system often needs more careful investigations. We further discuss how to use secure sketch in the design of multifactor authentication systems that allow easy revocation of user credentials.

A secure biometric authentication scheme based on robust hashing

In this paper, we propose a secure biometric based authentication scheme which fundamentally relies on the use of a robust hash function. The robust hash function is a one-way transformation tailored specifically for each user based on their biometrics. The function is designed as a sum of properly weighted and shifted Gaussian functions to ensure the security and privacy of biometric data. We discuss various design issues such as scalability, collision-freeness and security. We also provide test results obtained by applying the proposed scheme to ORL face database by designating the biometrics as singular values of face images.

Improvements on Sensor Noise Based Source Camera Identification

In a novel method for identifying the source camera of a digital image is proposed. The method is based on first extracting imaging sensors pattern noise from many images and later verifying its presence in a given image through a correlative procedure. In this paper, we investigate the performance of this method in a more realistic setting and provide results concerning its detection performance. To improve the applicability of the method as a forensic tool, we propose an enhancement over it by also verifying that class properties of the image in question are in agreement with those of the camera. For this purpose, we identify and compare characteristics due to demosaicing operation. Our results show that the enhanced method offers a significant improvement in the performance.

Feature extraction for a Slepian-Wolf biometric system using LDPC codes

We present an information-theoretically secure biometric storage system using graph-based error correcting codes in a Slepian-Wolf coding framework. Our architecture is motivated by the noisy nature of personal biometrics and the requirement to provide security without storing the true biometric at the device. The principal difficulty is that real biometric signals, such as fingerprints, do not obey the i.i.d. or ergodic statistics that are required for the underlying typicality properties in the Slepian-Wolf coding framework. To meet this challenge, we propose to transform the biometric data into binary feature vectors that are i.i.d. Bernoulli(0.5), independent across different users, and related within the same user through a BSC-p channel with small p< 0.5. Since this is a standard channel model for LDPC codes, the feature vectors are now suitable for LDPC syndrome coding. The syndromes serve as secure biometrics for access control. Experiments on a fingerprint database demonstrate that the system is information-theoretically secure, and achieves very low false accept rates and low false reject rates.

Tamper Detection Based on Regularity of Wavelet Transform Coefficients

Powerful digital media editing tools make producing good quality forgeries very easy for almost anyone. Therefore, proving the authenticity and integrity of digital media becomes increasingly important. In this work, we propose a simple method to detect image tampering operations that involve sharpness/blurriness adjustment. Our approach is based on the assumption that if a digital image undergoes a copy-paste type of forgery, average sharpness/blurriness value of the forged region is expected to be different as compared to the non-tampered parts of the image. The method of estimating sharpness/blurriness value of an image is based on the regularity properties of wavelet transform coefficients which involves measuring the decay of wavelet transform coefficients across scales. Our preliminary results show that the estimated sharpness/blurriness scores can be used to identify tampered areas of the image.

Secure sketch for biometric templates

There have been active discussions on how to derive a consistent cryptographic key from noisy data such as biometric templates, with the help of some extra information called a sketch. It is desirable that the sketch reveals little information about the biometric templates even in the worst case (i.e., the entropy loss should be low). The main difficulty is that many biometric templates are represented as points in continuous domains with unknown distributions, whereas known results either work only in discrete domains, or lack rigorous analysis on the entropy loss. A general approach to handle points in continuous domains is to quantize (discretize) the points and apply a known sketch scheme in the discrete domain. However, it can be difficult to analyze the entropy loss due to quantization and to find the “optimal” quantizer. In this paper, instead of trying to solve these problems directly, we propose to examine the relative entropy loss of any given scheme, which bounds the number of additional bits we could have extracted if we used the optimal parameters. We give a general scheme and show that the relative entropy loss due to suboptimal discretization is at most (nlog3), where n is the number of points, and the bound is tight. We further illustrate how our scheme can be applied to real biometric data by giving a concrete scheme for face biometrics.

eSketch: a privacy-preserving fuzzy commitment scheme for authentication using encrypted biometrics

The fuzzy commitment approach has gained popularity as away to protect biometric data used for identity verification of authentication. As it has been show recently, though, the use of fuzzy commitment is unavoidably linked to some leakage of information regarding the biometric template. An additional problem typical of authentication systems is that the user may want to protect his privacy, that is it would be desirable that the server only verifies whether the biometric template provided by the user is contained within the list of registered users without that the particular identity of the user accessing the system is revealed. The e-sketch protocol proposed in this paper, solves the above two problems by resorting to tools from Multi Party Computation relying on the additively homomorphic property of the underlying cryptosystem (e.e. the Paillers cryptosystem). The security and the complexity of the proposed protocol are discussed.

A geometric transformation to protect minutiae-based fingerprint templates

The increasing use of biometrics in different environments presents new challenges. Most importantly, biometric data are irreplaceable. Therefore, storing biometric templates, which is unique to individual user, entails significant security risks. In this paper, we propose a geometric transformation for securing the minutiae based fingerprint templates. The proposed scheme employs a robust one-way transformation that maps geometrical configuration of the minutiae points into a fixed-length code vector. This representation enables efficient alignment and reliable matching. Experiments are conducted by applying the proposed method on a synthetically generated minutiae point sets. Preliminary results show that the proposed scheme provides a simple and effective solution to the template security problem of the minutiae based fingerprint.

How to Protect Biometric Templates

In addition to the inherent qualities that biometrics posses, powerful signal processing tools enabled widespread deployment of the biometric-based identification/verification systems. However, due to the nature of biometric data, well-established cryptographic tools (such as hashing, encryption, etc.) are not sufficient for solving one of the most important problems related to biometric systems, namely, template security. In this paper, we examine and show how to apply a recently proposed secure sketch scheme in order to protect the biometric templates. We consider face biometrics and study how the performance of the authentication scheme would be affected after the application of the secure sketch. We further study the trade-off between the performance of the scheme and the bound of the entropy loss from the secure sketch.

Feature transformation of biometric templates for secure biometric systems based on error correcting codes

Secure storage of biometric templates is extremely important because a compromised biometric cannot be revoked and replaced an unlimited number of times. In many approaches proposed for secure biometric storage, an error correcting code (ECC) is applied to the enrollment biometric and the resulting parity or syndrome symbols are stored on the access control device, instead of the original biometric. The principal challenge here is that most standard ECCs are designed for memoryless channel statistics, whereas the variations between enrollment and probe biometrics have significant spatial correlation. To address this challenge, we propose to transform the original biometric into a feature vector that is explicitly matched to standard ECCs, thereby improving the security-robustness tradeoff of the overall biometric system. As a concrete example, we transform fingerprint minutiae maps into feature vectors compatible with ECCs designed for a binary symmetric channel. We conduct a statistical analysis of these feature vectors and show how our feature transformation algorithm may be combined with Low-Density Parity Check (LDPC) codes to obtain a secure fingerprint biometric system.