THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system

In this paper, we propose a new biometric verification and template protection system which we call the THRIVE system. The system includes novel enrollment and authentication protocols based on threshold homomorphic cryptosystem where the private key is shared between a user and the verifier. In the THRIVE system, only encrypted binary biometric templates are stored in the database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during the authentication stage. The THRIVE system is designed for the malicious model where the cheating party may arbitrarily deviate from the protocol specification. Since threshold homomorphic encryption scheme is used, a malicious database owner cannot perform decryption on encrypted templates of the users in the database. Therefore, security of the THRIVE system is enhanced using a two-factor authentication scheme involving the user's private key and the biometric data. We prove security and privacy preservation capability of the proposed system in the simulation-based model with no assumption. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form but she needs to proof her physical presence by using biometrics. The system can be used with any biometric modality and biometric feature extraction scheme whose output templates can be binarized. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biohash vectors on a desktop PC running with quad-core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real life applications

Fast and Accurate Likelihood Ratio Based Biometric Comparison in the Encrypted Domain

As applications of biometric verification proliferate, users become more vulnerable to privacy infringement. Biometric data is very privacy sensitive as it may contain information as gender, ethnicity and health conditions which should not be shared with third parties during the verification process. Moreover, biometric data that has fallen into the wrong hands often leads to identity theft. Secure biometric verification schemes try to overcome such privacy threats. Unfortunately, existing secure solutions either introduce a heavy computational or communication overhead or have to accept a high loss in accuracy; both of which make them impractical in real-world settings. This paper presents a novel approach to secure biometric verification aiming at a practical trade-off between efficiency and accuracy, while guaranteeing full security against honest-but-curious adversaries. The system performs verification in the encrypted domain using elliptic curve based homomorphic ElGamal encryption for high efficiency. Classification is based on a log-likelihood ratio classifier which has proven to be very accurate. No private information is leaked during the verification process using a two-party secure protocol. Initial tests show highly accurate results that have been computed within milliseconds range

A Criticism of the Current Security, Privacy and Accountability Issues in Electronic Health Records

Cryptography has been widely accepted for security and partly for privacy control as discovered from past works. However, many of these works did not provide a way to manage cryptographic keys effectively especially in EHR applications, as this is the Achilles heel of cryptographic techniques currently proposed. The issue of accountability for legitimate users also has not been so popular and only a few considered it in EHR. Unless a different approach is used, the reliant on cryptography and password or escrow based system for key management will impede trust of the system and hence its acceptability. Also users with right access should also be monitored without affecting the clinician workflow. This paper presents a detailed review of some selected recent approaches to ensuring security, privacy and accountability in EHR and gaps for future research were also identified.Comment: published (2014

Enhancing Privacy for Biometric Identification Cards

Most developed countries have started the implementation of biometric electronic identification cards, especially passports. The European Union and the United States of America struggle to introduce and standardize these electronic documents. Due to the personal nature of the biometric elements used for the generation of these cards, privacy issues were raised on both sides of the Atlantic Ocean, leading to civilian protests and concerns. The lack of transparency from the public authorities responsible with the implementation of such identification systems, and the poor technological approaches chosen by these authorities, are the main reasons for the negative popularity of the new identification methods. The following article shows an approach that provides all the benefits of modern technological advances in the fields of biometrics and cryptography, without sacrificing the privacy of those that will be the beneficiaries of the new system.Comment: 8 Pages, 7 figure

Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing

In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy serveral merits. Firstly, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable biometrics.Comment: 15 pages, 8 figures, 6 table

Iris Biometric Watermarking for Authentication Using Multiband Discrete Wavelet Transform and Singular-Value Decomposition

The most advanced technology, watermarking enables intruders to access the database. Various techniques have been developed for information security. Watermarks and histories are linked to many biometric techniques such as fingerprints, palm positions, gait, iris and speech are recommended. Digital watermarking is the utmost successful approaches among the methods available. In this paper the multiband wavelet transforms and singular value decomposition are discussed to establish a watermarking strategy rather than biometric information. The use of biometrics instead of conservative watermarks can enhance information protection. The biometric technology being used is iris. The iris template can be viewed as a watermark, while an iris mode of communication may be used to help information security with the addition of a watermark to the image of the iris. The research involves verifying authentication against different attacks such as no attacks, Jpeg Compression, Gaussian, Median Filtering and Blurring. The Algorithm increases durability and resilience when exposed to geometric and frequency attacks. Finally, the proposed framework can be applied not only to the assessment of iris biometrics, but also to other areas where privacy is critical

Enhancing Trust in eAssessment - the TeSLA System Solution

Trust in eAssessment is an important factor for improving the quality of online-education. A comprehensive model for trust based authentication for eAssessment is being developed and tested within the score of the EU H2020 project TeSLA. The use of biometric verification technologies to authenticate the identity and authorship claims of individual students in online-education scenarios is a significant component of TeSLA. Technical Univerity of Sofia (TUS) Bulgaria, a member of TeSLA consortium, participates in large-scale pilot tests of the TeSLA system. The results of questionnaires to students and teachers involved in the TUS pilot tests are analyzed and summarized in this work. We also describe the TeSLA authentication and fraud-detection instruments and their role for enhancing trust in eAssessment.Comment: Presented at the Conference on Technology Enhanced Assessment (TEA), 2018. 18 pages, 2 tables, 3 figure

Performance of the Fuzzy Vault for Multiple Fingerprints (Extended Version)

The fuzzy vault is an error tolerant authentication method that ensures the privacy of the stored reference data. Several publications have proposed the application of the fuzzy vault to fingerprints, but the results of subsequent analyses indicate that a single finger does not contain sufficient information for a secure implementation. In this contribution, we present an implementation of a fuzzy vault based on minutiae information in several fingerprints aiming at a security level comparable to current cryptographic applications. We analyze and empirically evaluate the security, efficiency, and robustness of the construction and several optimizations. The results allow an assessment of the capacity of the scheme and an appropriate selection of parameters. Finally, we report on a practical simulation conducted with ten users.Comment: This article represents the full paper of a short version to appear in the Proceedings of BIOSIG 2010 (copyright of Gesellschaft f\"ur Informatik

The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment

Mounting privacy legislation calls for the preservation of privacy in speech technology, though solutions are gravely lacking. While evaluation campaigns are long-proven tools to drive progress, the need to consider a privacy adversary implies that traditional approaches to evaluation must be adapted to the assessment of privacy and privacy preservation solutions. This paper presents the first step in this direction: metrics. We introduce the zero evidence biometric recognition assessment (ZEBRA) framework and propose two new privacy metrics. They measure the average level of privacy preservation afforded by a given safeguard for a population and the worst-case privacy disclosure for an individual. The paper demonstrates their application to privacy preservation assessment within the scope of the VoicePrivacy challenge. While the ZEBRA framework is designed with speech applications in mind, it is a candidate for incorporation into biometric information protection standards and is readily extendable to the study of privacy in applications even beyond speech and biometrics.Comment: submitted to Interspeech 202