Privacy-preserving Biometrics Research Articles

A Comprehensive Survey for Privacy-Preserving Biometrics: Recent Approaches, Challenges, and Future Directions

A Comprehensive Survey for Privacy-Preserving Biometrics: Recent Approaches, Challenges, and Future Directions

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 Biometrics Image Encryption and Digital Signature Technique Using Arnold and ElGamal

The scientific study of privacy-preserving biometrics, represented by the palmprint, face, and iris, has grown tremendously. That being said, there has not been much attention paid to the proper preservation, transmission, and authentication of biometric images used in everyday applications. In this paper, we propose a new complete model for encrypting and decrypting biometric images, including their signing and authentication, using a nested algorithm of 3D Arnold Transform. In addition, the ElGamal Encryption Algorithm for the encryption part and the ElGamal Digital Signature for the signature part are applied. The model is mainly based on the Arnold Transform and Public-Key Cryptosystem, which are convenient for key transfer and fully functional. Here, the model succeeds in encrypting and securing the authentication process for privacy-preserving biometric images. Various tests have been carried out to demonstrate the feasibility and security of the proposed model and have been compared with existing encryption methods to achieve better results. Moreover, the proposed model can also be extended to the storage, transmission, and authentication of biometric data for daily use.

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics.

The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.

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

A Privacy-Preserving Biometric Recognition System with Visual Cryptography

The popularity of more powerful and smarter digital devices has improved the quality of life and poses new challenges to the privacy protection of personal information. In this paper, we propose a biometric recognition system with visual cryptography, which preserves the privacy of biometric features by storing biometric features in separate databases. Visual cryptography combines perfect ciphers and secret sharing in cryptography with images, thus eliminating the complex operations in existing privacy-preserving schemes based on cryptography or watermarking. Since shares do not reveal any feature about biometric information, we can efficiently transmit sensitive information among sensors and smart devices in plain. To abate the influence of noise in visual cryptography, we leverage the generalization ability of transfer learning to train a visual cryptography-based recognition network. Experimental results show that our proposed method keeps the high accuracy of the feature recognition system when providing security.

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 .

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.

AGE: authentication in gadget-free healthcare environments

Mobile and sensor related technologies are significantly revolutionizing the medical and healthcare sectors. In current healthcare systems, gadgets are the prominent way of acquiring medical services. However, the recent technological advancements in smart and ambient environments are offering users new ways to access the healthcare services without using any explicit gadgets. One of the key challenges in such gadget-free environments is performing secure user authentication with the intelligent surroundings. For example, a secure, efficient and user-friendly authentication mechanism is essential for elderly/disabled people or patients in critical conditions requiring medical services. Hence, modern authentication systems should be sophisticated enough to identify such patients without requiring their physical efforts or placing gadgets on them. This paper proposes an anonymous and privacy-preserving biometrics based authentication scheme for such gadget-free healthcare environment. We performed formal security verification of our proposed scheme using CDVT /AD tool and our results indicate that the proposed scheme is secure for such smart and gadget-free environments. We verify that the proposed scheme can resist against various well-known security attacks. Moreover, the proposed system showed better performance as compared with existing biometrics based remote user authentication schemes.

Secure Hamming distance computation for biometrics using ideal-lattice and ring-LWE homomorphic encryption

ABSTRACTWith widespread development of biometrics, concerns about security and privacy are rapidly increasing. Homomorphic encryption enables us to operate on encrypted data without decryption, and it can be applied to construct a privacy-preserving biometric system. In this article, we apply two homomorphic encryption schemes based on ideal-lattice and ring-LWE (Learning with Errors), which both have homomorphic correctness over the ring of integers of a cyclotomic field. We compare the two schemes in applying them to privacy-preserving biometrics. In biometrics, the Hamming distance is used as a metric to compare two biometric feature vectors for authentication. We propose an efficient method for secure Hamming distance. Our method can pack a biometric feature vector into a single ciphertext, and it enables efficient computation of secure Hamming distance over our packed ciphertexts.

Privacy-preserving biometrics authentication systems using fully homomorphic encryption

Purpose – The purpose of this paper is to determine the effectiveness of using fully homomorphic encryption (FHE) to preserve the privacy of biometric data in an authentication system. Biometrics offers higher accuracy for personal recognition than traditional methods because of its properties. Biometric data are permanently linked with an individual and cannot be revoked or cancelled, especially when biometric data are compromised, leading to privacy issues. Design/methodology/approach – By reviewing current approaches, FHE is considered as a promising solution for the privacy issue because of its ability to perform computations in the encrypted domain. The authors studied the effectiveness of FHE in biometric authentication systems. In doing so, the authors undertake the study by implementing a protocol for biometric authentication system using iris. Findings – The security analysis of the implementation scheme demonstrates the effectiveness of FHE to protect the privacy of biometric data, as unlimited ...

Privacy Preserving Biometric Verification

Template and databases are very important part of biometric system and attacker mostly attacks on template and database of biometic system, so securing them is very important. Size of the database is very large so reduce database size is also very crucial issue these days . Our aim is to provide priva- cy to the user by protecting template and to reduce dimension of the template database for effective computations.With this aim in mind we have proposed Classical Multidimensional scaling technique which is very effective in dimension reduction as well as it also provides privacy to the user by securing template . We have implemented biometric system using PCA, RP, PCARP and Classical MDS.Experimental results show that proposed technique is more effective in dimesion reduction as well as it also improves the recognition rate of the biometric sys- tem.Comparative analysis of Proposed method with PCA ,RP,and PCARP are also discussed.

FourFactor based Privacy Preserving Biometric Authentication and Authorization Scheme for Enhancing Grid Security

Increase in the data sharing of distributed data has resulted in resource usage being more and more distributed. The distributed nature of data and its need to be accessed has resulted in the grid environment. Authentication is considered as the first step of security requirement for any grid environment against potential threats. This paper proposes an authentication method which is based not only on the password and the user ID but also on the biometric input and the position of the user. By enhancing the security at the entry point of Virtual Organization (VO), the grid environment is secured in its first step. The same biometric and position data used for authentication can further be used for authorization purposes so as to reduce the cost and time of storing different data for different purposes. A FourFactor based Privacy Preserving Biometric (4F2PB) authentication scheme for a grid environment is proposed which can work on the existing Network Framework. The proposed authentication scheme optimises the security required for the entry level user and prevents malicious user from entering into the grid environment.

Sharp lower bounds on the extractable randomness from non-uniform sources

Extraction of uniform randomness from (noisy) non-uniform sources is an important primitive in many security applications, e.g. (pseudo-)random number generators, privacy-preserving biometrics, and key storage based on Physical Unclonable Functions. Generic extraction methods exist, using universal hash functions. There is a trade-off between the length of the extracted bit string and the uniformity of the string. In the literature there are proven lower bounds on this length as a function of the desired uniformity. The best known bound involves a quantity known as smooth min-entropy. Unfortunately, there exist at least three definitions of smooth entropy. In this paper we compare three of these definitions, and we derive improved lower bounds on the extractable randomness. We also investigate the use of almost universal hash functions, which are slightly worse at extracting randomness than universal hash functions, but are preferable in practice because they require far less resources in devices. We show that using them has negligible effect on the extractable randomness.

An Analysis of Random Projection for Changeable and Privacy-Preserving Biometric Verification

Changeability and privacy protection are important factors for widespread deployment of biometrics-based verification systems. This paper presents a systematic analysis of a random-projection (RP)-based method for addressing these problems. The employed method transforms biometric data using a random matrix with each entry an independent and identically distributed Gaussian random variable. The similarity- and privacy-preserving properties, as well as the changeability of the biometric information in the transformed domain, are analyzed in detail. Specifically, RP on both high-dimensional image vectors and dimensionality-reduced feature vectors is discussed and compared. A vector translation method is proposed to improve the changeability of the generated templates. The feasibility of the introduced solution is well supported by detailed theoretical analyses. Extensive experimentation on a face-based biometric verification problem shows the effectiveness of the proposed method.