Nalini K. Ratha

Bio: Nalini K. Ratha is an academic researcher from IBM. The author has contributed to research in topics: Biometrics & Fingerprint recognition. The author has an hindex of 50, co-authored 216 publications receiving 12290 citations. Previous affiliations of Nalini K. Ratha include Michigan State University & University at Buffalo.

Securing biometric data through template distribution

Abstract: Embodiments include method, systems and computer program products for safeguarding biometric data. Aspects include receiving a first biometric data unit and generating a template based upon the first biometric data unit. Aspects also include sending the template to a plurality of external template storage devices, each template storage device having a unique device identifier. Aspects also include generating a biometric query including a second biometric data unit. Aspects also include sending the biometric query to at least some of the plurality of external template storage devices. Aspects also include receiving a match score from at least one of the plurality of template storage devices external to the processor, wherein the match score reflects the degree of similarity between the first biometric data unit and the second biometric data unit.

Controlling access to electronic resources based on a user's sociometric identification document

Abstract: A method establishes a session between a network resource and a user device used by a user having a particular sociometric identity. One or more processors identify an interaction between a user and one or more provider entities. The processor(s) identify profiles for the one or more provider entities, and compute a sociometric identity of the user based on the profiles of the one or more provider entities with which the user has had an interaction. One or more processors transmit the sociometric identity to a network resource in order to establish a session between the network resource and a user device used by the user having the sociometric identity.

The Common Biometrics

Abstract: In this chapter we provide a brief description of the six most widely used (or widely discussed) biometrics. These most commonly used automated biometric identifiers are (i) finger, (ii) face, (iii) voice (speaker recognition), (iv) hand geometry, (v) iris, and (vi) signature. Chapter 4 describes other biometrics that are not currently as common.

Efficient Encrypted Inference on Ensembles of Decision Trees.

Abstract: Data privacy concerns often prevent the use of cloud-based machine learning services for sensitive personal data. While homomorphic encryption (HE) offers a potential solution by enabling computations on encrypted data, the challenge is to obtain accurate machine learning models that work within the multiplicative depth constraints of a leveled HE scheme. Existing approaches for encrypted inference either make ad-hoc simplifications to a pre-trained model (e.g., replace hard comparisons in a decision tree with soft comparators) at the cost of accuracy or directly train a new depth-constrained model using the original training set. In this work, we propose a framework to transfer knowledge extracted by complex decision tree ensembles to shallow neural networks (referred to as DTNets) that are highly conducive to encrypted inference. Our approach minimizes the accuracy loss by searching for the best DTNet architecture that operates within the given depth constraints and training this DTNet using only synthetic data sampled from the training data distribution. Extensive experiments on real-world datasets demonstrate that these characteristics are critical in ensuring that DTNet accuracy approaches that of the original tree ensemble. Our system is highly scalable and can perform efficient inference on batched encrypted (134 bits of security) data with amortized time in milliseconds. This is approximately three orders of magnitude faster than the standard approach of applying soft comparison at the internal nodes of the ensemble trees.

Secure Fingerprint Authentication

Abstract: Biometrics-based authentication systems offer advantages over the present practices of knowledge and/or possession-based authentication systems. However, when using biometrics, the overall authentication architecture needs to be reexamined to ensure that no new weak security points are introduced. After analyzing a pattern recognition-based threat model of a biometrics authentication system, this chapter describes secure fingerprint authentication. Several solutions are proposed to alleviate the threats using conventional encryption as well as novel techniques that exploit the richness of biometrics data. The proposed methods are applicable in many application areas. These includes system security, electronic commerce security, point of sale, point of entry/exit and point of access. We also argue that an authentication scheme with both smart card and biometrics improves the overall security of a system.

Data clustering: a review

Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

Handbook of Fingerprint Recognition

Abstract: A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

Fingerprint image enhancement: algorithm and performance evaluation

Abstract: In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.