Reviewerly/exHarmony huggingface dataset:
Blockchain and IoT in Pharmaceutical Supply Chains

Pharmaceutical drugs can be considered as one of the most important needs of human societies. The spread of Coronavirus disease (COVID-19) has dramatically increased the volume of drug exchanges and the role of pharmaceutical supply chains in society for providing drugs has become recognized, but one of the most important concerns in this area is trust and confidence to the authenticity of drugs. Developing an accurate system for tracking productions in pharmaceutical supply chains is necessary. These advances can be used for improving the quality of pharmaceutical supply chains. Using IoT, it is possible to track the status of productions in supply chains. Also, blockchain technology can be used for recording and sharing data transparently. So, this integration of IoT and blockchain technology can reduce concerns about the authenticity of drugs and prevent their distribution. Given the important role of medicine for improving the health of society, this chapter explores the role of IoT devices and blockchain in the successful development of pharmaceutical supply chains.
Towards Securing Information in the Digital Era Using Blockchain Technology

The world is full of data and the value of extracted knowledge is high in the digital society. As the value of data increases, threats of privacy and data breach have also increased. In today's businesses, there is a major requirement for technology that can manage and secure data. Blockchain technology is one such attempt towards improvement of information security in businesses. Blockchain is an assortment of secure, shared, and appropriated records connected with one another that empowers recording and following assets without the need of a unified confided in power. Each additional transaction in the blockchain contains a hash esteem from the past block. In the event that the substance of the transaction is adjusted, it will change the hash worth of the current transaction. This makes blockchain unchanging. In this way, blockchain first validates the transaction and safeguards it, and at last, these entries are preserved as records.
Blockchain Implementation in Food Retail Supply Chain Management

Blockchain technology is expected to play a pivotal role in innovation across various sectors in the near future, facilitating long-term and sustainable growth for businesses and the economy. The food retail sector, in particular, can greatly benefit from blockchain technology, as food safety remains a top priority for stakeholders, particularly consumers. This chapter examines the advantages of blockchain technology in the food retail sector and presents a case study of a Portuguese food retail corporation. The case study explores whether the company should expedite the adoption of blockchain technology in its supply chain management. The conclusions drawn from this study help managers in making informed decisions regarding future strategy implementation, intending to enhance supply chain efficiency, safety, and transparency. Furthermore, these findings will provide valuable insights for other players in the food retail sector grappling with similar challenges.
Performance Evaluation and Analysis of Different Association Rule Mining (ARM) Algorithms

Data processing technique of data mining has the power to identify patterns and relationships in huge volumes of data from multiple sources to make decisions to drive the world in the current scenario. Association rule mining (ARM) is the most significant method used in data mining. This approach is employed to find trends in the database that are typical. The field of ARM has seen a lot of activities. ARM remains a source of concern for various experts. There are algorithms that assess fundamental factors like precision, algorithm speed, and data assistance. The ARM algorithms, namely AprioriHybrid, AIS, AprioriTID, and Apriori, as well as FP-Growth, are examined in this work. This chapter provides a comparison of different algorithms utilized for association rules mining against several performance factors.
Emerging Blockchain Applications in Mobility

Blockchain is one of the major technologies that are expected to disruptively change the entire mobility ecosystem. Through blockchain's synergies with artificial intelligence, services, internet of things (IoT), and connectivity, smart and connected mobility services are going to be enabled for the better. More environment friendly, safer, and affordable solutions are emerging, transforming mobility from sole vehicle ownership and related centralized data platforms to a multi-trillion-dollar novel applications ecosystem in a distributed environment with reliable identity and immutable time-stamped records. There is no single solution that can fit diverse areas of implementation with different needs. Moreover, blockchain solutions bring together a number of risks based on the implementation type. This chapter identifies the suitable blockchain types specific to potential use cases in the transforming mobility ecosystem. In addition to preferable blockchain types and consensus mechanisms based on application needs, related risks are discussed in detail.
A Related Key Attack on the Word-Oriented BeepBeep Stream Cipher

The word-oriented BeepBeep stream cipher, developed by Driscoll in FSE 2002, is proposed to provide integrity and confidentiality for embedded systems. There has been no attack on BeepBeep published until now. By exploiting a weakness of the BeepBeep stream cipher during its initialization, this paper presents a key recovery attack on the BeepBeep stream cipher in the related key setting. The attack recovers the 192-bit secret key of BeepBeep with a time complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{128}$</tex> , requiring two related keys, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{32}$</tex> chosen IVs and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{33}$</tex> keystream words. This is the first cryptanalytic attack on BeepBeep which is significantly better than the exhaustive key search. The result shows that the BeepBeep stream cipher is vulnerable against the related key attack and can not provide the 192-bit security.
Efficient Privacy-Preserving Data Aggregation for Lightweight Secure Model Training in Federated Learning

Federated learning has been widely adopted in every aspect of our daily life to well protect the dataset privacy, since the model parameters are trained locally and aggregated to global one, but the data themselves are not required to be sent to servers as traditional machine learning. In State Grid, different power companies tend to cooperate to train a global model to predict the risk of the grid or the trustworthiness of the customers in the future. The datasets belonging to each power company should be protected against another corporation, sector or other unauthorized entities, since they are closely related to users' privacy. On the other hand, it is widely reported even the local mode parameters can also be exploited to launch several attacks such as membership inference. Most existing work to realize privacy-preserving model aggregation relies on computationally intensive public key homomorphic encryption(HE) such as Paillier's cryptosystem, which loads intolerably high complexity on resource-constrained local users. To address this challenging issue, in this paper, a lightweight privacy-preserving data aggregation scheme is proposed without utilizing public-key homomorphic encryption. First, an efficient privacy-preserving data aggregation protocol PPDA is proposed based on any one-way trapdoor permutation in the multiple user setting. Then, based on PPDA, a lightweight secure model training scheme LSMT in federated learning is designed. Finally, security analysis and extensive simulations show that our proposed PPDA and LSMT well protect the sensitive data of power enterprises from collusion attacks, guarantees the security of aggregated results, and outperforms existing ones in terms of computational and communication overhead.
Cryptomining Traffic Detection Based on BiGRU and Attention Mechanism

The increasing popularity of cryptocurrencies has led to a rise in cryptomining attacks, where attackers unauthorizedly use the victim's computer resources to mine digital currency. This brings significant financial losses and security risks to both personal and professional life. Therefore, the detection of cryptomining attacks is of paramount importance. The conventional packet inspection technique is no longer effective due to the use of encryption. Moreover, the prevalent machine learning methods rely heavily on features extracted by professional experience, which is time-consuming. In this paper, we analyze the features of real-world campus cryptomining traffic and propose an end-to-end deep learning model for malicious mining detection. Our model, based on Bidirectional Gate Recurrent Unit (BiGRU) with an attention mechanism, extracts representative features from the raw flow. The results indicate that our approach outperforms benchmark models and previous methods on the large-scale imbalanced dataset, achieving a G-mean value of 0.99 with only 8 packets of a flow.
A Multi-Strategy Adversarial Attack Method for Deep Learning Based Malware Detectors

Deep learning allows building high-accuracy malware detectors without complicated feature engineering. However, research shows that the deep learning model is vulnerable and can be deceived if attackers add perturbation to input samples to craft adversarial examples deliberately. By altering the pixel values of the images, attackers have been able to generate adversarial examples that can fool state-of-the-art deep learning based image classifiers. However, Windows malware is a structured binary program file. Therefore, arbitrarily altering its contents will often break the program's functionality. In order to solve this problem, a standard but inefficient method is to run the sample in the sandbox to verify whether its functionality is preserved. This paper proposes a multi-strategy adversarial attack method, which can generate malware adversarial examples with functionality-preserving. Our method manipulates the redundant or extended space in the Windows malware binary, so it will not break functionality. Experiments show that our method has a high attack success rate and efficiency.
NIC Fingerprint-Based Switch Access Control Technology

Almost all existing access control systems authenticate end users based on their digital characteristics, such as MAC addresses. Since digital features are easily forged, these access control systems cannot secure the network well. In this paper, we propose an access control technology based on Ethernet network interface controller (NIC) fingerprint, a physical characteristic, to achieve identity authentication. At the switch side, the physical layer signals from the terminal NIC are collected, and the fingerprint of the NIC is extracted from the physical layer signals using the least mean square error (LMS) adaptive filter. On the basis of MAC address authentication, the proxy mechanism of Remote Authentication Dial In User Service (RADIUS) protocol is adopted to add the NIC fingerprint in the password field of the RADIUS request message, which enables the authentication server to perform two-factor authentication based on the NIC fingerprint and MAC address. The experimental results showed that the recognition accuracy for 75 NICs is 96.6%. In this paper, an access control system was built using a switch, a signal collector, a proxy server and an authentication server to realize that the terminal user was allowed to access the network only when both the NIC fingerprint and the MAC address were legal, which verified the feasibility of the scheme.
Secure Search over Multi-key Homomorphically Encrypted Data

Homomorphic Encryption (HE) is a very attractive solution to ensure privacy when outsourcing confidential data to the cloud, as it enables computation on the data without de-cryption. However HE starts to lose effectiveness when scaled to multiple parties. In this paper, we propose the first multi-key HE search and computation framework. To achieve an efficient set-up for multi-party search and compute, we explore the different approaches to multi-key HE and secure search schemes to reduce rounds of communication. We propose a novel framework to search homomorphically encrypted data outsourced to a semi-honest server and shared with multiple parties dynamically using proxy re-encryption schemes. Our framework performs search with linear search complexity with just one round of communication between the two parties. The protocol provides multi-hop ca-pabilities that enable further computations on the search results.
haydIT : An Encryptor and Decryptor Application

Encryption is a method of hiding data so that it cannot be read by anyone who does not know the key. The key is used to lock and unlock data. In a hack-prone society like ours now, this method would prevent unauthorized individuals from understanding your most protected correspondences. Hence, the development of an encryptor and decryptor is necessary. haydIT offers a user-friendly interface using intuitive icons that most of us are familiar with. Users can encrypt data, generate private key that serves as a unique lock of encryption, and send it thru any means to its recipient. Receivers can also use the same system to decrypt the data using the private key that must be manually entered to the system. haydIT prides itself in supporting the conversion of different language scriptures, providing asymmetrical way of data conversion through double encryption of randomly selected characters. It also supports multi- level of encryption and applies parity checking for data integrity. Thus, provides a virtually- pattern-less way of encoding and decoding data. The developers utilized the Spiral Methodology in developing the project. It combines the elements of both design and prototyping-in-stages, in an effort to combine advantages of top- down and bottom-up concepts. The developed PC app was tested by 15 IT Professionals based on several metrics and proved that haydIT is performing efficiently as expected with a grand mean of 4.75 interpreted as Excellent. With haydIT, everyone can guarantee that messages will be understood only by its intended reader.
White-Box PRNG: A Secure Pseudo-Random Number Generator under the White-Box Attack Model

The random number generator (RNG) plays a crucial role in modern cryptography. While true RNG (TRNG) is available, pseudo RNG (PRNG) is often preferred due to its better compatibility. However, PRNGs have long been vulnerable to the leakage of internal states, which compromises their properties of resilience, forward security, and backward security. Furthermore, this threat will become more prevalent as adversaries gain full control of the PRNG. Inspired by white-box cryptography, we aim to provide a definition of white-box PRNG that protects against the leakage of internal states. Additionally, we bind the white-box PRNG with a specific application to resist code-lifting attacks. We implement the white-box PRNG based on various types of white-box SM4 ciphers and measure their storage overhead and random number generation speed. Meanwhile, we evaluate the randomness of the generated numbers using randomness test standards, including NIST SP 800-90B and GM/T 0005–2021, and compare the testing results to the output of Linux entropy pool and OpenSSL.
Hill Cipher Modifications and Dynamic Cryptosystem Design

This paper proposes a highly scalable dynamic cryptosystem, Modified Affine Hill Cipher (AHC-M), which effectively addresses the known vulnerabilities of the classical Hill Cipher and provides an innovative design approach to the development of modern cryptography. By analyzing the existing Hill Cipher variations, the key concepts and design principles, such as non-linear encryption, dynamic key expansion, non-square matrix algebra and dynamic cryptosystem are investigated in detail. Building on these concepts, two practical modifications are proposed that can significantly improve computational complexity and enhance security. We also propose a cryptanalysis technique by extending the chosen-plaintext attack, which can be applied to break the Affine Hill Cipher and serves as a motivation towards the proposed cryptosystem. Lastly, these concepts are generalized as the starting point for further research.
Verifiable Threshold Multiplication Protocol based on Oblivious Transfer

Shamir Secrets Sharing (SSS) is a foundational element of many Multi-Party Computation (MPC) protocols. Although SSS has the ability to handle linear combinations of multiple secrets natively, its ability to handle the multiplication of secrets is limited. In general, the multiplication of secrets requires more participants, due to the increased “degree”. In this paper, we present a verifiable method for handling the multiplication of SSS without increasing its degree. Our method is based on the Gilboa Protocol or its variants, which are built on top of Oblivious Transfer (OT). We also provide a security analysis, demonstrating that the method is secure under the assumption of a malicious adversary security model. As an application use case, we present a new ECDSA threshold signature scheme built on top of our method.
A Survey on Cross-Chain Data Transfer

Blockchain technology is moving towards multichain interconnection, i.e., various blockchains sharing data, assets and functions to collaborate. To enable different blockchains to work together, Cross-chain Data Transfer technology is significant and developing rapidly, attracting the attention of both industry and academia. This paper defines Cross-chain Data Transfer (CDT) at the level of technical goals, explains the unique importance of CDT and discuss schemes for designing CDT approaches. We collect the latest approaches that have been applied in the field and analyze their advantages and disadvantages. Moreover, we discuss future challenges and research directions, show the broad research prospects in the field of CDT technology.
A New Research on Verifiable and Searchable Encryption Scheme Based on Blockchain

Cloud storage attracts more and more individuals and enterprises to outsource and store data on cloud servers due to its advantages of high efficiency, speed, low economic cost and on-demand access. Due to privacy requirements, data files need to be uploaded after being encrypted. Current searchable encryption technology enables retrieval of encrypted data, but lacks verification of searched results. In response to the above problem, this paper proposes a new searchable encryption scheme based on blockchain, which supports multi-keyword ranked search together with verification of searched results. Concretely, the scheme first encrypts the data and uploads it to the cloud server, then builds the index via blockchain. By calling the smart contracts to execute the search algorithm, the search result is returned to the user and the hash value is verified, which ensures the integrity and accuracy of the searched result. Secondly, by combining the vector space model and BM25 model to construct the index and query of encrypted data, the ranked search for multiple keywords is realized, in which the keyword balanced binary tree index is established to improve the retrieval efficiency. Experimental results show that the improved scheme has higher search accuracy while ensuring retrieval efficiency.
AES 128 Encrypted Image Classification

The homomorphic cryptographic operations is an umbrella term for computation performed on encrypted data without explicit decryption. The purpose of these operations is to manipulate encrypted data without having to apply decryption first and therefore minimize the computational overhead, breach of anonymity, privacy and without having to disclose private content. One of the promising prospects of homomorphic cryptography is the data classification using neural networks mounted to the back-planes of computational clouds or IoT sensors. While several approaches already explored the classification of encrypted data on specific ciphers, it is yet not well known how well such tasks can be performed on the state of the art AES encryption which was never designed to be homomorphic. In order to provide some insight on this topic, we investigate three different aspects of the classification of AES encrypted data: end-to-end learning, transfer learning and the ability of learning the cipher in the context of classification. We compare the performance of network models trained using transfer learning with end-to-end trained models on encrypted data. We also evaluate the classification of encrypted images using Invertible Neural Network (INN) as a mean to learn and predict the encryption of the data, as well as to determine if the learned AES can be efficiently learned. Finally using INN, we evaluate the learning and memorization extent of the encryption: we perform cross-data validation on different combinations of MNIST datasets such as handwritten digits, fashion images and handwritten letters.
Computation on Jacobians of Hyperelliptic Curves of Genus 3

In this article, we give an easy method to distinguish different cases of additions on Jacobians of hyperelliptic curves of genus 3. In addition, we give an advanced algorithm for group laws on Jacobian of hyperelliptic curves of genus 3. By this method, our algorithm can handle all kinds of inputs without recalling a generic algorithm. Our method is mainly based on Harley's algorithm. However, we use linear algebra over finite fields, instead of Chinese Reminder Theorem over function fields. Moreover, We did <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times 10^{8}$</tex> experiments in the finite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{61}-1}$</tex> , our algorithm runs 0.033% faster than previous works in general addition.
An Improved Key Mismatch Attack on Kyber

Recently, American National Institute of Standards and Technology (NIST) announced Kyber as the first KEM candidate to be standardized. The security of Kyber is based on the modular learning with errors (MLWE) problem, which achieves excellent efficiency and size. This work proposes an improved key mismatch on Kyber, which can reduce the number of queries required to recover the secret key. We first transform the problem of finding a certain parameter of ciphertexts into a quantum ordered search problem. Then we give the procedure of finding the value of a parameter in the ciphertexts by the quantum method. Finally, we instantiate this attack method on Kyber512, Kyber768 and Kyber1024. Compared with the existing attack algorithm, our improved attack reduces the number of queries for Kyber512, Kyber768 and Kyber1024 by 63%, 59% and 45%, respectively.
Present Role of Artificial Intelligence in Software Project Management and in the Future

In the future, Artificial intelligence will bring the rapid development and boost in the IT industry. It will cover the knowledge area, the development area and risks within the project. Artificial intelligence is the best application that will help to get the finest goals. This application is the finest application that do not need cost or any resource for input. Artificial intelligence will assist the project managers in cost, time and data analytics. In this paper, we will survey the IT industries of Pakistan and get feedback from the IT project managers through questionnaire. This paper will also explore the future working of artificial intelligence with project manager. This will help to get the objectives of the participation of AI enabled Software Project Management (SPM) and future prediction.