Privacy-preserving Biometric Identification Research Articles

PEBASI: A Privacy preserving, Efficient Biometric Authentication Scheme based on Irises

We introduce a novel privacy-preserving biometric authentication scheme based on irises that allows a user to enroll once at a trusted biometric certification authority (BCA) and authenticate to online service providers (SPs) multiple times without involving the BCA during the authentication. Our scheme preserves the user’s biometric privacy from the SPs and transactional privacy from the BCA, while providing security against a malicious user. During the enrollment, the BCA issues a signed token that encrypts the user’s biometrics. We introduce techniques enabling the SP and the user to perform secure computation of biometric matching between such encrypted biometrics and the user’s biometrics captured at the authentication time. We provide a prototype implementation, a performance evaluation, and a security analysis of the protocol.

A Fast RLWE-Based IPFE Library and its Application to Privacy-Preserving Biometric Authentication

With the increased use of data and communication through the internet and the abundant misuse of personal data by many organizations, people are more sensitive about their privacy. Privacy-preserving computation is becoming increasingly important in this era. Functional encryption allows a user to evaluate a function on encrypted data without revealing sensitive information. Most implementations of functional encryption schemes are too time-consuming for practical use. Mera <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> first proposed an inner product functional encryption scheme based on ring learning with errors to improve efficiency. In this work, we optimize the implementation of their work and propose a fast inner product functional encryption library. Specifically, we identify the main performance bottleneck, which is the number theoretic transformation based polynomial multiplication used in the scheme. We also identify the micro and macro level parallel components of the scheme and propose novel techniques to improve the efficiency using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">open multi-processing</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">advanced vector extensions 2</i> vector processor. Compared to the original implementation, our optimization methods translate to 89.72%, 83.06%, 59.30%, and 53.80% improvements in the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Setup</b></monospace> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Encrypt</b></monospace> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>KeyGen</b></monospace> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> , and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Decrypt</b></monospace> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> operations respectively, in the scheme for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">standard</b> security level. Designing privacy-preserving applications using functional encryption is ongoing research. Therefore, as an additional contribution to this work, we design a privacy-preserving biometric authentication scheme using inner product functional encryption primitives.

Privacy-Preserving Biometric Authentication: Cryptanalysis and Countermeasures

In this paper, we cryptanalyzed a Verifiable Threshold Predicate Encryption (VTPE) enabled Privacy-Preserving Biometric Authentication (PPBA) protocol reported in IEEE-TDSC and revealed discrepancies between its security claims and our security analysis. To be precise, the underlying authentication and key agreement scheme which is based on a challenge-response mechanism and watermark signal unsatisfactorily meets the following security scenario: (a) resistance to man-in-the-middle attacks, (b) biometric template protection, and (c) user anonymity and untraceability. To address these issues, we utilize Physical Unclonable Functions (PUF) to design a PUF driven Verifiable Threshold Predicate Encryption (PUF-VTPE) scheme and a secure PPBA protocol. The PUF-VTPE-based PPBA protocol equips with dual authentication using biometric and mobile device, which offers strong authenticity before establishing the session key. Simultaneously, the non-invertible property of PUF protects the biometric templates in the physical layer. The proposed storage-free mechanism that hides the challenge of device PUF in biometric template alleviates data leakage caused by storage challenges in PUF-based authentication protocols. Moreover, the experimental analysis suggests that the proposed PPBA protocol possesses ISO/IEC 24745 criteria of non-invertibility, unlinkability, and revocability. Additionally, the proposed PPBA protocol reduces the computational cost by about 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared to that of the cryptanalyzed scheme.

A novel privacy-preserving biometric authentication scheme.

Most existing secure biometric authentication schemes are server-centric, and users must fully trust the server to store, process, and manage their biometric data. As a result, users' biometric data could be leaked by outside attackers or the service provider itself. This paper first constructs the EDZKP protocol based on the inner product, which proves whether the secret value is the Euclidean distance of the secret vectors. Then, combined with the Cuproof protocol, we propose a novel user-centric biometric authentication scheme called BAZKP. In this scheme, all the biometric data remain encrypted during authentication phase, so the server will never see them directly. Meanwhile, the server can determine whether the Euclidean distance of two secret vectors is within a pre-defined threshold by calculation. Security analysis shows BAZKP satisfies completeness, soundness, and zero-knowledge. Based on BAZKP, we propose a privacy-preserving biometric authentication system, and its evaluation demonstrates that it provides reliable and secure authentication.

Attacks and Countermeasures on Privacy-Preserving Biometric Authentication Schemes

Based on the Threshold Predicate Encryption (TPE), the biometric authentication scheme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PassBio</i> aims to correctly authenticate genuine end-users without leaking their biometric privacy information. However, this article proposes two impersonation attacks to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PassBio</i> by merely sending very few query messages. Specifically, an attacker is able to cheat the authentication server with probability 50% by sending the server a random query, or almost 100% by sending the server a collusion of old genuine queries, without being identified. Moreover, in order to defeat the impersonation attacks, this article presents a Verifiable Threshold Predicate Encryption (VTPE) scheme which includes three components: (1) a multi-segment TPE for reducing the computational cost and communication overhead significantly; (2) a segment-wise watermarking for defeating the random attacks; and (3) a challenge-response mechanism for defeating the replay and collusion attacks. In addition, the watermarking also creates a secure channel between the querying user and the server. The experiments on both simulated feature vectors and real face images demonstrate that the present attacks and countermeasures are effective and efficient.

Privacy-preserving bimodal authentication system using Fan-Vercauteren scheme

Privacy-preserving bimodal authentication system using Fan-Vercauteren scheme

Mobile Finger Print Verification and Automatic Log in Platform Using Blockchain

Abstract: Biometric identification has become increasingly popular in recent years. With the development of cloud computing, database owners are motivated to outsource the large size of biometric data and identification tasks to the cloud to get rid of the expensive storage and computation costs, which however brings potential threats to users’ privacy. In this paper, we propose an efficient and privacy-preserving biometric identification outsourcing scheme. Specifically, the biometric data is encrypted and outsourced using Block Chain. To execute a biometric identification, the database owner encrypts the query data and submits it to the cloud. The Block Chain performs identification operations over the encrypted database and returns the result to the database owner. A thorough security analysis indicates the proposed scheme is secure even if attackers can forge identification requests and collude with the cloud

A Novel Biometric Identification Scheme Based on Zero-Knowledge Succinct Noninteractive Argument of Knowledge

Biometric identification is a convenient and reliable method in identity authentication. The widespread adoption of biometric identification requires strong privacy protection against possible theft or loss of biometric data. Existing techniques for privacy-preserving biometric identification mainly rely on traditional cryptographic technology such as oblivious transfer and homomorphic encryption, which will incur huge expenses to the system and cannot be applied to large-scale practical applications. For these issues, we propose a biometric identification scheme by constructing zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK). Our scheme not only reduces the communication overhead, which only needs to send 8 constants to the verifier but also can protect the fingerprint template from disclosure. The time complexity of proof generation and proof verification are about O(C) and O x + log C , respectively, and the size of the proof is only 8 constants, where C and x represent the size of the circuit and the public input, respectively. We have implemented the proposed authentication solution on a public data set of fingerprint images and evaluated the performance and security.

Privacy Preserving Biometric Authentication on the blockchain for smart healthcare

Privacy Preserving Biometric Authentication (PPBA) schemes are designed for anonymous authentication of patients to protect patient’s privacy in accessing healthcare services. Recently, blockchain technology in healthcare has emerged as a new research area to provide tamper-resistance and non-repudiation in e-health systems. One aspect of this research could lead to blockchain-based secure biometric identification for smart healthcare, which may face the paradox of anonymous biometric authentication on public blockchains. In this paper, we describe an efficient, fully anonymous and GDPR-compliant PPBA protocol built into the blockchain of any privacy coin such as Monero. The new protocol provides encrypted offline storage and processing in the encrypted domain. The infrastructure necessary for the online authentication is outsourced to the public blockchain that provides integrity of its data. In addition to auditing capabilities for misbehaving entities, the new system reduces the number of transactions necessary for authentication and enables revocation of biometric identities. We provide new PPBA schemes both for set difference/overlap and Euclidean distance metrics without using bilinear pairings, where the former leads to an efficient solution to the compatibility for organ transplant. We limit the generation of encrypted templates for public testing even if biometric/health data is of low min-entropy. Due to the anonymity of the cryptocurrency, we break the link between the stealth address of an authenticating user and its biometrics. We describe the user and identity privacy notions independent of the underlying privacy coin and guarantee the security of our proposal in the framework of those generic notions. Finally, we simulate the new proposal on Monero blockchain and analyze the transaction fees required for hill climbing attacks. The results show that our design leads to a natural hindrance against these attacks that could be successful even if the templates are stored as encrypted. To the best of our knowledge, this is the first efficient blockchain-based PPBA scheme that exhibits a punishment against hill climbing attacks through transaction fees. • PPBA on the blockchain solving the secure identification problem in e-health • Security against hill climbing attacks via transaction fees • Two new schemes without pairings providing an efficient solution for organ matching • Limiting generation of encrypted templates even if biometrics have low min-entropy • Revocation of biometrics and reduced number of transactions for authentication

Efficient Biometric Identification on the Cloud With Privacy Preservation Guarantee

Benefited from its reliability and convenience, biometric identification has become one of the most popular authentication technologies. Due to the sensitivity of biometric data, various privacy-preserving biometric identification protocols have been proposed. However, the low computational efficiency or the security vulnerabilities of these protocols limit their wide deployment in practice. To further improve the efficiency and enhance the security, in this paper, we propose two new privacy-preserving biometric identification outsourcing protocols. One mainly utilizes the efficient Householder transformation and permutation technique to realize the high-efficiency intention under the known candidate attack model. The other initializes a novel random split technique and combines it with the invertible linear transformation to achieve a higher security requirement under the known-plaintext attack model. Also, we argue the security of our proposed two protocols with a strict theoretical analysis and, by comparing them with the prior existing works, comprehensively evaluate their efficiency.

Comments on “PassBio: Privacy-Preserving User-Centric Biometric Authentication”

In 2018, Zhou et al. proposed PassBio, a privacy-preserving biometric authentication scheme, in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (TIFS) (DOI: 10.1109/TIFS.2018.2838540). PassBio delivers its promise and effectively protects users&#x2019; privacy from malicious servers. However, we demonstrate that PassBio may be vulnerable in a more inclusive attack model with malicious clients.

An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.

Multimodal biometric authentication for mobile edge computing

In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) system designed for Mobile Edge Computing (MEC) and multimodal biometrics . We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user’s secret parameters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead . Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing .

SviaB: Secure and verifiable multi‐instance iris remote authentication using blockchain

Homomorphic encryption (HE) is the most widely explored research area in the construction of privacy-preserving biometric authentication systems because of its advantages over cancellable biometrics and biometric cryptosystems. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save computational resources, resulting in a false accept/reject. To address this, secure and verifiable multi-instance iris authentication using blockchain (SviaB) is proposed. Paillier HE provides confidentiality for the iris templates in SviaB. The blockchain offers the integrity of the encrypted reference iris templates as well as the trust of the comparator result. The challenges of using blockchain in biometrics are also addressed in SviaB. Extensive experimental results on benchmark iris databases demonstrate that SviaB provides privacy to the iris templates with no loss of accuracy and trust in the comparator result.

Privacy-preserving and verifiable multi-instance iris remote authentication using public auditor

Homomorphic Encryption (HE) is the most widely explored research area to construct privacy-preserving biometric authentication systems due to its advantages over cancelable biometrics and biometric cryptosystem. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save the computational resources results in false accept/reject. To address this, we propose a privacy-preserving and verifiable multi-instance iris authentication using public auditor (PviaPA). Paillier HE provides confidentiality for the iris templates in PviaPA. A public auditor ensures the correctness of comparator result in PviaPA. Extensive experimental results on benchmark iris databases demonstrate that PviaPA provides privacy to the iris templates with no loss in the accuracy as well as trust on the comparator result.

Privacy-Preserving Identification Systems With Noisy Enrollment

In this paper, we study fundamental trade-offs in privacy-preserving biometric identification systems with noisy enrollment. The proposed identification systems include helper data, secret keys, and private keys. Helper data are stored in a public database and used for identification. Secret keys are either stored in a secure database or provided to the user, and can be used in a next step, e.g. for authentication. Private keys are provided by users, and are also used for identification. In this paper, we impose a noisy enrollment channel and an arbitrarily small privacy and secrecy leakage rate. We characterize the optimal trade-off among the identification, secret key, private key, and helper data rates. Depending on how secret keys are produced, we study two cases of the proposed privacy-preserving identification systems, where the secret keys are generated and chosen respectively. By introducing private keys, it is shown that the identification system achieves close to zero privacy leakage rate in both generated and chosen secret key settings. The results also show that the identification rate and the secret key rate can be enlarged by increasing the private key rate. This work provides a framework for analyzing privacy-preserving identification systems and an insight on the design of optimal systems.

Biometrics and Privacy-Preservation: How Do They Evolve?

The emerging field of privacy-preserving biometrics is attracting significant attention, as a paradigm that can address several of the key concerns in cryptographic authentication processes, whilst simultaneously addressing the issues of privacy. This paper contributes to the understanding of the field from following perspectives: (1) It proposes a novel and comprehensive taxonomy of privacy-preserving biometrics for the classification of the knowledge/existing literature in the field. (2) It provides a taxonomy-guided literature survey. (3) Open research problems/future works are discussed. (4) As the techniques used in privacy-preserving biometric authentication systems rely upon, or integrate with, general biometric matching techniques, a taxonomy and summary of the state-of-art biometrics matching techniques has also been developed. Such system-level knowledge organization will help produce excellent self-contained contents of reference materials for researchers in both the biometrics community and cryptography community who would otherwise have difficulty in understanding the relevant materials from the other community.

Significance of the Transition to Biometric Template Protection: Explore the Future

The IT security paradigm evolves from secret-based to biometric identity-based. Biometric identification has gradually become more popular in recent years for handheld devices. Privacy-preserving is a key concern when biometrics is used in authentication systems in the present world today. Nowadays, the declaration of biometric traits has been imposed not only by the government but also by many private entities. There are no proper mechanisms and assurance that biometric traits will be kept safe by such entities. The encryption of biometric traits to avoid privacy attacks is a giant problem. Hence, state-of-the-art safety and security technological solutions must be devised to prevent the loss and misuse of such biometric traits. In this paper, we have identified different cancelable biometrics methods with the possible attacks on the biometric traits and directions on possible countermeasures in order to design a secure and privacy-preserving biometric authentication system. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation and Index of max hashing. We tested and evaluated the proposed novel method on a standard dataset and achieved good results.

An Efficient Biometric Identification in Cloud Computing With Enhanced Privacy Security

Biometric identification has developed rapidly in recent years because of its convenience and reliability. Due to the sensitivity of biometric data, many privacy-preserving biometric identification schemes have been put forward, exploiting either homomorphic encryption or matrix-transformation. However, existing schemes based on homomorphic encryption generally suffer from low computational efficiency, and existing matrix-transformation-based schemes are insufficiently secure. In this paper, we demonstrate that the matrix-transformation-based privacy-preserving biometric identification scheme recently proposed by Zhu et al. is vulnerable to a known-plaintext attack (KPA). To remedy this security flaw, we propose a new privacy-preserving biometric identification scheme, in which the property of the orthogonal matrix and additional randomness are utilized. Security analysis and comparisons indicate that our scheme can resist not only the KPA attack but also the more powerful chosen-plaintext attack (CPA), which is a reasonable attack in practice. Moreover, our scheme enjoys higher computational efficiency than other similar schemes, which implies our scheme can better support a huge database for practical biometric identification, and it also enhances privacy security of sensitive biometric data.

Outsourced Biometric Identification With Privacy

Biometric identification typically scans a large-scale database of biometric records for finding a close enough match of an individual. This paper investigates how to outsource this computationally expensive scanning while protecting the privacy of both the database and the computation. Exploiting the inherent structures of biometric data and the properties of identification operations, we first present a privacy-preserving biometric identification scheme which uses a single server. We then consider its extensions in the two-server model. It achieves a higher level of privacy than our single-server solution assuming two servers are not colluding. Apart from somewhat homomorphic encryption, our second scheme uses batched protocols for secure shuffling and minimum selection. Our experiments on both synthetic and real data sets show that our solutions outperform existing schemes while preserving privacy.