A systematic literature review of blockchain-based applications: Current status, classification and open issues

In recent years, the rapid urbanization of world’s population causes many economic, social, and environmental problems, which affect people’s living conditions and quality of life significantly. The concept of “smart city” brings opportunities to solve these urban problems. The objectives of smart cities are to make the best use of public resources, provide high-quality services to the citizens, and improve the people’s quality of life. Information and communication technology plays an important role in the implementation of smart cities. Blockchain as an emerging technology has many good features, such as trust-free, transparency, pseudonymity, democracy, automation, decentralization, and security. These features of blockchain are helpful to improve smart city services and promote the development of smart cities. In this paper, we provide a comprehensive survey on the literature involving blockchain technology applied to smart cities. First, the related works and background knowledge are introduced. Then, we review how blockchain technology is applied in the realm of smart cities, from the perspectives of smart citizen, smart healthcare, smart grid, smart transportation, supply chain management, and others. Finally, some challenges and broader perspectives are discussed.

A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges

A blockchain database employs cryptography and other methods to implement and protect a distributed, publicly-amendable ledger. Transactions in a blockchain ledger are intentionally anonymous; however, there are cases where it would be useful to be able to verify or disprove a claim of identity of a contributor of a blockchain transaction. Biometrics can be used to link a human being to digital information using their unique physical traits in a way that is analogous to a handwritten or digital signature. An exemplary embodiment disclosed herein describes methods to create and store data in a blockchain transaction such that it can be used in the future to biometrically verify the identity of the contributor of the transaction, and use encoded biometric data to determine whether the blockchain transaction was created or not created by a particular individual.

Biometric verification of a blockchain database transaction contributor

The blockchain is an emerging technology. Because of its efficiency and functionality, it is widely considered to have revolutionary application prospects. As a supporting part of the data structure, the hash function is important for ensuring the availability and security of the blockchain. To evaluate the security of blockchain technology it is important to analyze several security criteria of the hash functions used in the blockchain. This paper focuses on two security criteria-hiding and puzzle-friendliness-that the hash function should satisfy in the blockchain proposed by Narayanan et al. (Narayanan A, Bonneau J, Felten E, et al. Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press, 2016). Under the framework of Rogaway?Shrimpton's theory, their definitions are mathematically described and their relation is proved. Also, comparisons between the two criteria and traditional ones in hash functions are made. The conclusions of this paper point out that for hash functions, it is harder to break hiding and puzzle-friendliness than to break preimage resistance, which shows that hash functions proven to be preimage-resistant or tested by preimage cryptanalysis like SHA256 tend to be enough to be used in the corresponding parts of the blockchain design. At the same time, from the attackers' view the conclusions can provide a theoretical basis for studying the potential attacks on and defects that may exist in the structure of the hash functions applied in the blockchain.

Research on the Security Criteria of Hash Functions in the Blockchain

Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy–enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy–related research in the area of biometrics and review existing work on Biometric Privacy–Enhancing Techniques (B–PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B–PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

/pdf/privacy-enhancing-face-biometrics-a-comprehensive-survey-2wfk78ga6m.pdf

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

In contrast to electronic travel documents (e.g. ePassports), the standardisation of breeder documents (e.g. birth certificates), regarding harmonisation of content and contained security features is in statu nascendi. Due to the fact that breeder documents can be used as an evidence of identity and enable the application for electronic travel documents, they pose the weakest link in the identity life cycle and represent a security gap for identity management. In this work, we present a cost efficient way to enhance the long-term security of breeder documents by utilizing blockchain technology. A conceptual architecture to enhance breeder document long-term security and an introduction of the concept's constituting system components is presented. Our investigations provide evidence that the Bitcoin blockchain is most suitable for breeder document long-term security.

Enhancing Breeder Document Long-Term Security Using Blockchain Technology

Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade Through a touchless acquisition process, many issues of touch-based systems are circumvented, eg, the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface However, touchless fingerprint recognition systems reveal new challenges In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks Also, further issues, eg, interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups Many works have been proposed so far to put touchless fingerprint recognition into practice Published approaches range from self identification scenarios with commodity devices, eg, smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenariosThis work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges An overview of available research resources completes the work

/pdf/an-overview-of-touchless-2d-fingerprint-recognition-16uwu29njo.pdf

An overview of touchless 2D fingerprint recognition

The data security and privacy of enrolled subjects is a critical requirement expected from biometric systems. This paper addresses said topic in facial biometric identification. In order to fulfil the properties of unlinkability, irreversibility, and renewability of the templates required for biometric template protection schemes, homomorphic encryption is utilised. In addition to achieving the aforementioned objectives, the use of homomorphic encryption ensures that the biometric performance remains completely unaffected by the template protection scheme.The main contributions of this paper are: It proposes an architecture of a system capable of performing biometric identification in the encrypted domain, as well as provides and evaluates an implementation using two existing homomorphic encryption schemes. Furthermore, it discusses the pertinent technical considerations and challenges in this context.

On the Application of Homomorphic Encryption to Face Identification

This work presents an automated touchless fingerprint recognition system for smartphones. We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system. Also, our implementation is made publicly available for research purposes. During a database acquisition, a total number of 1,360 touchless and touch-based samples of 29 subjects are captured in two different environmental situations. Experiments on the acquired database show a comparable performance of our touchless scheme and the touch-based baseline scheme under constrained environmental influences. A comparative usability study on both capturing device types indicates that the majority of subjects prefer the touchless capturing method. Based on our experimental results we analyze the impact of the current COVID-19 pandemic on fingerprint recognition systems. Finally, implementation aspects of touchless fingerprint recognition are summarized.

Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects.

Machine learning (ML) models have become increasingly important components of many software systems. Therefore, ensuring their privacy and security is a crucial task. Current research mainly focuses on the development of security and privacy methods. However, ML practitioners, as the individuals in charge of translating the theory into practical applications, have not yet received much attention. In this paper, the security and privacy awareness and practices of ML practitioners are studied through an online survey with the aim of (1) gaining insight into the current state of awareness, (2) identifying influencing factors, and (3) exploring the actual use of existing methods and tools. The results indicate a relatively low general privacy and security awareness among the ML practitioners surveyed. In addition, they are less familiar with ML privacy protection methods than with general security methods or ML-related ones. Moreover, awareness correlates with the years of working with ML but not with the level of academic education or the field of occupation. Finally, the practitioners in this study seem to experience uncertainties in implementing legal frameworks, such as the European General Data Protection Regulation, into their ML workflows.

Nicolas Buchmann

Papers

Enhancing Breeder Document Long-Term Security Using Blockchain Technology

An overview of touchless 2D fingerprint recognition

On the Application of Homomorphic Encryption to Face Identification

Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects.

“I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners