Trustworthy authorization method for security in Industrial Internet of Things
روش مجوز معتبر برای امنیت در اینترنت اشیا صنعتی-2021
Industrial Internet of Things (IIoT) realizes machine-to-machine communication and human–computer inter- action (HCI) through communication network, which makes industrial production automatic and intelligent. Security is critical in IIoT because of the interconnection of intelligent industrial equipment. In IIoT environment, legitimate human–computer interaction can only be performed by authorized professionals, and unauthorized access is not tolerated. In this paper, a reliable authentication method based on biological information is proposed. Specifically, the complete local binary pattern (CLPB) and the statistical local binary pattern (SLPB) are introduced to describe the local vein texture characteristics. Meanwhile, the contrast energy and frequency domain information are regarded as auxiliary information to interpret the finger vein. The distance between the features of the registration image and the test image is used to recognize the finger vein image, so as to realize identity authentication. The experiments are carried out on SDUMLA-FV database and FV-USM database, and results show that the presented method has achieved high recognition accuracy.
Keywords: Industrial Internet of Things (IIoT) | Human–computer interaction (HCI) | Biometric recognition | Comprehensive texture | Security system
Efficient biometric-based identity management on the Blockchain for smart industrial applications
مدیریت هویت مبتنی بر بیومتریک کارآمد در Blockchain برای کاربردهای صنعتی هوشمند-2021
In this work, we propose a new Blockchain-based Identity Management system for smart industry. First, we describe an efficient biometric-based anonymous credential scheme, which supports selective disclosure, suspension/thaw and revocation of credentials/entities. Our system provides non-transferability through a freshly computed hidden biometric attribute, which is generated using a secure fuzzy extractor during each authentication. This mechanism combined with offchain storage guarantees GDPR compliance, which is required for protecting user’s data. We define blinded (Brands) DLRep scheme to provide multi-show unlinkability, which is a lacking feature in Brands’ credential based systems. For larger organizations, we re-design the system by replacing the Merkle Tree with an accumulator to improve scalability. The new system enables auditing by adapting the standard Industrial IoT (IIoT) Identity Management Lifecycle to Blockchain. Finally, we show that the new proposal outperforms BASS, i.e. the most recent blockchain-based anonymous credential scheme designed for smart industry. The computational cost at the user-side (can be a weak IoT device) of our scheme is 8-times less than that of BASS. Thus, our system is more suitable for IIoT.© 2020 Elsevier B.V. All rights reserved.
Keywords: Identity management | Smart industry | Blockchain | Non-transferability | Biometrics | DLRep | Multi-show unlinkability | Selective disclosure | Accumulators
Social movements, identity and disruption in organizational fields: Accounting for farm animal welfare
جنبش های اجتماعی، هویت و اختلال در زمینه های سازمانی: حسابداری برای رفاه حیوانات مزرعه-2021
In this study we provide evidence on how accounting disclosures can motivate social movement organizations (SMOs) to create a new source of normativity in an organizational field, to impact upon firms through identity, image and culture. The source of normativity, the Business Benchmark on Farm Animal Welfare (BBFAW), was created as a means of accounting for farm animal welfare by food companies. Working at the intersection of theories relating to organizational fields, social movements and organizational identity, we investigate how the SMOs create the conditions for change through their framing of farm animal welfare, collective action and the mobilization of resources. Ideas such as institutional agency and institutional control are introduced to explain the power dynamics that enable change. By interpreting the organizational field as a relational space, identity, self-interest and intermittently-active fields provide further constructs to explain behaviour. Evidence from BBFAW reports and publications demonstrates how the NGOs employed a multi-period strategy to effect change. A longitudinal company case study provides an illustration of the cascade of the movement, demonstrating that there is more than an alignment of accounting disclosures. New business opportunities arise, requiring a realignment of strategy, a redesign of organizational architecture and participation of stakeholders. We illustrate our findings through the creation of a framework which could be employed more widely to study of sources of normativity in a relational field. This paper shows that accounting disclosures have a role to play in creating a new normativity that generates social change.
keywords: رفاه حیوانات | هویت | هنجار | فیلترهای سازمانی و جنبش های اجتماعی | Animal welfare | Identity | Normativity | Organizational ﬁelds and social movements
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG
Human identification driven by deep CNN and transfer learning based on multiview feature representations of ECG-2021
Increasingly smart techniques for counterfeiting face and fingerprint traits have increased the potential threats to information security systems, creating a substantial demand for improved security and better privacy and identity protection. The internet of Things (IoT)-driven fingertip electrocardiogram (ECG) acquisition provides broad application prospects for ECG-based identity systems. This study focused on three major impediments to fingertip ECG: the impact of variations in acquisition status, the high computational complexity of traditional convolutional neural network (CNN) models and the feasibility of model migration, and a lack of sufficient fingertip samples. Our main contribution is a novel fingertip ECG identification system that integrates transfer learning and a deep CNN. The proposed system does not require manual feature extraction or suffer from complex model calculations, which improves its speed, and it is effective even when only a small set of training data exists. Using 1200 ECG recordings from 600 individuals, we consider 5 simulated yet potentially practical scenarios. When analyzing the overall training accuracy of the model, its mean accuracy for the 540 chest- collected ECG from PhysioNet exceeded 97.60 %, and for 60 subjects from the CYBHi fingertip-collected ECG, its mean accuracy reached 98.77 %. When simulating a real-world human recognition system on 5 public datasets, the validation accuracy of the proposed model can nearly reach 100 % recognition, outperforming the original GoogLeNet network by a maximum of 3.33 %. To some degree, the developed architecture provides a reference for practical applications of fingertip-collected ECG-based biometric systems and for information network security.
Keywords: Off-the-person | Fingertip ECG biometric | Human identification | Convolutional neural network (CNN) | Transfer learning
A cancelable biometric authentication system based on feature-adaptive random projection
یک سیستم احراز هویت بیومتریک قابل لغو بر اساس طرح تصادفی سازگار با ویژگی-2021
Biometric template data protection is critical in preventing user privacy and identity from leakage. Random projection based cancelable biometrics is an efficient and effective technique to achieve biometric template protection. However, traditional random projection based cancelable template design suffers from the attack via record multiplicity (ARM), where an adversary obtains multiple transformed templates from different applica- tions and the associated parameter keys so as to assemble them into a full-rank linear equation system, thereby retrieving the original feature vector. To address this issue, in this paper we propose a feature-adaptive random projection based method, in which the projection matrixes, the key to the ARM, are generated from one basic matrix in conjunction with local feature slots. The generated projection matrixes are discarded after use, thus making it difficult for the adversary to launch the ARM. Moreover, the random projection in the proposed method is performed on a local-feature basis. This feature-adaptive random projection can mitigate the negative impact of biometric uncertainty on recognition accuracy, as it limits the error to part of the transformed feature vector rather than the entire vector. The proposed method is evaluated on four public available databases FVC2002 DB1-DB3 and FVC2004 DB2. The experimental results and security analysis show the validity of the proposed method.
Keywords: Biometric authentication | Template protection | Random projection | Cancelable biometrics
ECB2: A novel encryption scheme using face biometrics for signing blockchain transactions
ECB2: یک طرح رمزگذاری جدید با استفاده از بیومتریک چهره برای امضای تراکنش های بلاک چین-2021
Blockchain is the technology on the basis of the recent smart and digital contracts. It ensures at this system the required characteristics to be effectively applied. In this work, we propose a novel encryption scheme specifically built to authorize and sign transactions in digital or smart contracts. The face is used as a biometric key, encoded through the Convolutional Neural Network (CNN), FaceNet. Then, this encoding is fused with an RSA key by using the Hybrid Information Fusion algorithm (BNIF). The results show a combined key that ensures the identity of the user that is executing the transaction by preserving privacy. Experiments reveal that, even in strong heterogeneous acquisition conditions for the biometric trait, the identity of the user is ensured and the contract is properly signed in less than 1.86 s. The proposed ECB2 encryption scheme is also very fast in the user template creation (0.05s) and requires at most four attempts to recognize the user with an accuracy of 94%.
Design of a fingerprint template protection scheme using elliptical structures
طراحی طرح محافظت از اثر انگشت با استفاده از ساختارهای بیضوی-2021
Although biometric authentication is viewed as more prominent than password or token-based methodology in identity verification, biometric templates are vulnerable to attacks. This paper proposes a new approach for securing fingerprint templates using elliptical structures generated from the fingerprint minutiae. Authors generate a feature vector from the ellipse and will be projected onto a 3D-space to compute a binary string. The resultant binary string is transformed to frequency domain (DFT) and multiplied with a user specific random matrix to make it permanently non-invertible and secure. The results show the efficacy of the proposed method for protecting the fingerprints. c 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Fingerprint | Ellipse | Discrete Fourier transform | Template protection
Corporate accounting information disclosure based on FPGA and neural network
افشای اطلاعات حسابداری شرکت بر اساس FPGA و شبکه عصبی-2021
Corporate accounting information is a measure of the company’s accounting and external reporting systems. It is routinely disclosed, which is quantitative data on its financial position and performance audit. The corporate accounting information system contains confidential information that needs to be secured. The consequences of unauthorized access are data loss from identity theft issues. To solve the problem, encrypt and decrypt the sensitive corporate accounting information and product the data using the proposed algorithm Neural Network (NN) and Field Programmable Gate Array (FPGA) is used to classify the corporate accounting information authorized person and unauthorized person. When one authorized user accesses the corporate account infor- mation, it generates the secret critical process. The proposed algorithm unauthorized person cannot access the information is not allowed for stealing. Encryption is the process of converting to something as random and meaningless as direct text data. Decryption is the process of restoring the ciphertext plaintext.
keywords: اطلاعات حسابداری شرکت | شبکه عصبی (NN) | fpga | فرد مجاز | شخص غیر مجاز | رمزگذاری | رمزگشایی | Corporate accounting information | Neural network (NN) | FPGA | Authorized person | Unauthorized person | Encryption | Decryption
A review on speaker recognition: Technology and challenges
مروری بر تشخیص گوینده: فناوری و چالش ها-2021
Voice is a behavioral biometric that conveys information related to a person’s traits, such as the speaker’s ethnicity, age, gender, and feeling. Speaker recognition deals with recognizing the identity of people based on their voice. Although researchers have been working on speaker recognition in the last eight decades, advancements in technology, such as the Internet of Things (IoT), smart devices, voice assistants, smart homes, and humanoids, have made its usage nowadays trendy. This paper provides a comprehensive review of the literature on speaker recognition. It discusses the advances made in the last decade, including the challenges in this area of research. This paper also highlights the system and structure of speaker recognition as well as its feature extraction and classifiers. The use of speaker recognition in applications is also presented. As recent studies showed the possibility of fooling machine learning into giving an incorrect pre-diction; thus, the adversarial attack is also discussed. The aim is to enhance researchers’ under-standing in the area of speaker recognition.
Keywords: Biometric | Open system | Speaker recognition | Text-independent | Feature extraction | Classifier | Machine learning | Adversarial attack
Comments on biometric-based non-transferable credentials and their application in blockchain-based identity management
نظرات در مورد اعتبارنامه های غیرقابل انتقال مبتنی بر بیومتریک و کاربرد آنها در مدیریت هویت مبتنی بر بلاک چین-2021
In IT-ecosystems, access to unauthorized parties is prevented with credential-based access control techniques (locks, RFID cards, biometrics, etc.). Some of these methods are ineffective against malicious users who lend their credentials to other users. To obtain non-transferability, Adams proposed a combination of biometrics encapsulated in Pedersen commitment with Brands digital credential. However, Adams’ work does not consider the Zero Knowledge Proof-of Knowledge (ZKPoK) system for Double Discrete Logarithm Representation of the credential. Besides, biometrics is used directly, without employing any biometric cryptosystem to guarantee biometric privacy, thus Adams’ work cannot be GDP compliant. In this paper, we construct the missing ZKPoK protocol for Adam’s work and show its inefficiency. To overcome this limitation, we present a new biometric-based nontransferable credential scheme that maintains the efficiency of the underlying Brands credential. Secondly, we show the insecurity of the first biometric-based anonymous credential scheme designed by Blanton et al.. In this context, we present a brute-force attack against Blanton’s biometric key generation algorithm implemented for fuzzy vault. Next, we integrate an Oblivious PRF (OPRF) protocol to solve the open problem in Blanton’s work and improve its efficiency by replacing the underlying signature scheme with PS-signatures. Finally, we evaluate application scenarios for non-transferable digital/anonymous credentials in the context of Blockchain-based Identity Management (BBIM). We show that our modified constructions preserve biometric privacy and efficiency, and can easily be integrated into current BBIM systems built upon efficient Brands and PS-credentials.
Keywords: Biometrics security | Non-transferability | Digital credentials | Anonymous credentials | Fuzzy vault | Fuzzy extractors | Double discrete logarithm (DDL) | Brands DLRep | Selective disclosure | Blockchain | Identity management