Biblio

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality — all of which are necessary for completely automated commercial AI systems.

With the advent of ease of access to the internet and an increase in digital literacy among citizens, digitization of the banking sector has throttled. Countries are now aiming for a cashless society. The introduction of a Unified Payment Interface (UPI) by the National Payments Corporation of India (NPCI) in April 2016 is a game-changer for cashless models. UPI payment model is currently considered the world’s most advanced payment system, and we see many countries adopting this cashless payment mode. With the increase in its popularity, there arises the increased need to strengthen the security posture of the payment solution. In this work, we explore the privacy challenges in the existing data flow of UPI models and propose approaches to preserve the privacy of customers using the Unified Payments Interface.

With the ubiquitous advancement in smart medical devices and systems, the potential of Remote Patient Monitoring (RPM) network is evolving in modern healthcare systems. The medical professionals (doctors, nurses, or medical experts) can access vitals and sensitive physiological information about the patients and provide proper treatment to improve the quality of life through the RPM network. However, the wireless nature of communication in the RPM network makes it challenging to design an efficient mechanism for secure communication. Many authentication schemes have been proposed in recent years to ensure the security of the RPM network. Pseudonym, digital signature, and Authenticated Key Exchange (AKE) protocols are used for the Internet of Medical Things (IoMT) to develop secure authorization and privacy-preserving communication. However, traditional authentication protocols face overhead challenges due to maintaining a large set of key-pairs or pseudonyms results on the hospital cloud server. In this research work, we identify this research gap and propose a novel secure and efficient privacy-preserving authentication scheme using cuckoo filters for the RPM network. The use of cuckoo filters in our proposed scheme provides an efficient way for mutual anonymous authentication and a secret shared key establishment process between medical professionals and patients. Moreover, we identify the misbehaving sensor nodes using a correlation-based anomaly detection model to establish secure communication. The security analysis and formal security validation using SPAN and AVISPA tools show the robustness of our proposed scheme against message modification attacks, replay attacks, and man-in-the-middle attacks.

Software vulnerabilities are closely monitored by the security community to timely address the security and privacy issues in software systems. Before a vulnerability is published by vulnerability management systems, it needs to be characterized to highlight its unique attributes, including affected software products and versions, to help security professionals prioritize their patches. Associating product names and versions with disclosed vulnerabilities may require a labor-intensive process that may delay their publication and fix, and thereby give attackers more time to exploit them. This work proposes a machine learning method to extract software product names and versions from unstructured CVE descriptions automatically. It uses Word2Vec and Char2Vec models to create context-aware features from CVE descriptions and uses these features to train a Named Entity Recognition (NER) model using bidirectional Long short-term memory (LSTM) networks. Based on the attributes of the product names and versions in previously published CVE descriptions, we created a set of Expert System (ES) rules to refine the predictions of the NER model and improve the performance of the developed method. Experiment results on real-life CVE examples indicate that using the trained NER model and the set of ES rules, software names and versions in unstructured CVE descriptions could be identified with F-Measure values above 0.95.

Developers of mobile applications rely on the trust of their customers. On the one hand the requirement exists to create feature-rich and secure apps, which adhere to privacy standards to not deliberately disclose user information. On the other hand the development process must be streamlined to reduce costs. Here third-party libraries come into play. Inclusion of many, possibly nested libraries pose security risks, app-creators are often not aware of. This paper presents a way to combine free open-source tools to support developers in checking their application that it does not induce security issues by using third-party libraries. The tools FlowDroid, Frida, and mitm-proxy are used in combination in a simple and viable way to perform checks to identify privacy leaks of third-party apps. Our proposed setup and configuration empowers average app developers to preserve user privacy without being dedicated security experts and without expensive external advice.

The problem of privacy protection of trajectory data has received increasing attention in recent years with the significant grow in the volume of users that contribute trajectory data with rich user information. This creates serious privacy concerns as exposing an individual's privacy information may result in attacks threatening the user's safety. In this demonstration we present TP$^\textrm3$ a novel practical framework for supporting trajectory privacy preservation in Mobile Cloud Environments (MCEs). In TP$^\textrm3$, non-expert users submit their trajectories and the system is responsible to determine their privacy exposure before sharing them to data analysts in return for various benefits, e.g. better recommendations. TP$^\textrm3$ makes a number of contributions: (a) It evaluates the privacy exposure of the users utilizing various privacy operations, (b) it is latency-efficient as it implements the privacy operations as serverless functions which can scale automatically to serve an increasing number of users with low latency, and (c) it is practical and cost-efficient as it exploits the serverless model to adapt to the demands of the users with low operational costs for the service provider. Finally, TP$^\textrm3$'s Web-UI provides insights to the service provider regarding the performance and the respective revenue from the service usage, while enabling the user to submit the trajectories with recommended preferences of privacy.

Any Big Data scenario eventually reaches scalability concerns for several factors, often storage or computing power related. Modern solutions have been proven to be effective in multiple domains and have automated many aspects of the Big Data pipeline. In this paper, we aim to present a solution for deploying event-based automated data processing tools for low code environments that aim to minimize the need for user input and can effectively handle common data processing jobs, as an alternative to distributed solutions which require language specific libraries and code. Our architecture uses a combination of a network exposed service with a cluster of “Data Workers” that handle data processing jobs effectively without requiring manual input from the user. This system proves to be effective at handling most data processing scenarios and allows for easy expandability by following simple patterns when declaring any additional jobs.

The paper introduces and develops the new concept of current-mode multifunctional circuit, a computational structure that is able to implement, using the same functional core, a multitude of circuit functions: amplifying, squaring, square-rooting, multiplying, exponentiation or generation of any continuous mathematical function. As a single core computes a large number of circuit functions, the original approach of analog signal processing from the perspective of multifunctional structures presents the important advantages of a much smaller power consumption and design costs per implemented function comparing with classical designs. The current-mode operation, associated with the original concrete implementation of the proposed structure increase the accuracy of computed functions and the frequency behaviour of the designed circuit. Additionally, the temperature-caused errors are almost removed by specific design techniques. It will be also shown a new method for third-order approximating the exponential function using an original approximation function. A generalization of this method will represent the functional basis for realizing an improved accuracy function synthesizer circuit with a simple implementation in CMOS technology. The proposed circuits are compatible with low-power low voltage operations.

We discuss the performance of a new quantumsafe multivariate digital signature scheme proposed recently, called the Multivariate Polynomial Public Key Digital Signature (MPPK DS) scheme. Leveraging MPPK KEM or key exchange mechanism, the MPPK DS scheme is established using modular exponentiation with a randomly chosen secret base from a prime field. The security of the MPPK DS algorithm largely benefits from a generalized safe prime associated with the said field and the Euler totient function. We can achieve NIST security levels I, III, and V over a 64-bit prime field, with relatively small public key sizes of 128 bytes, 192 bytes, and 256 bytes for security levels I, III, and V, respectively. The signature sizes are 80 bytes for level I, 120 bytes for level III, and 160 bytes for level V. The MPPK DS scheme offers probabilistic procedures for signing and verification. That is, for each given signing message, a signer can randomly pick a base integer to be used for modular exponentiation with a private key, and a verifier can verify the signature with the digital message, based on the verification relationship, using any randomly selected noise variables. The verification process can be repeated as many times as the verifier wishes for different noise values, however, for a true honest signature, the verification will always pass. This probabilistic feature largely restricts an adversary to perform spoofing attacks. In this paper, we conduct some performance analyses by implementing MPPK DS in Java. We compare its performance with benchmark performances of NIST PQC Round 3 finalists: Rainbow, Dilithium, and Falcon. Overall, the MPPK DS scheme demonstrates equivalent or better performance, and much smaller public key, as well as signature sizes, compared to the three NIST PQC Round 3 finalists.

With the increasing awareness of privacy protection and data security, people’s concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. The experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier.

Online template attacks (OTA), high-efficiency side-channel attacks, are initially presented to attack the elliptic curve scalar. The modular exponentiation is similarly vulnerable to OTA. The correlation between modular multiplication's intermediate products is a crucial leakage of the modular exponent. This paper proposed a practical OTA countermeasure based on randomized domain Montgomery multiplication, which combines blinding and shuffling methods to eliminate the correlation between modular multiplication's inner products without additional computation requirements. The proposed OTA countermeasure is implemented on the Sakura-G board with a suppose that the target board and template board are identical. The experiment results show that the proposed countermeasure is sufficient to protect the modular exponentiation from OTA.

Analyzing reflectionally symmetric features inside an image is one of the important processes for recognizing the peculiar appearance of natural and man-made objects, biological patterns, etc. In this work, we will point out an efficient detector of reflectionally symmetric shapes by addressing a class of projection-based signatures that are structured by a generalized \textbackslashmathcalR\_fm-transform model. To this end, we will firstly prove the \textbackslashmathcalR\_fmˆ-transform in accordance with reflectional symmetry detection. Then different corresponding \textbackslashmathcalR\_fm-signatures of binary shapes are evaluated in order to determine which the corresponding exponentiation of the \textbackslashmathcalR\_fm-transform is the best for the detection. Experimental results of detecting on single/compound contour-based shapes have validated that the exponentiation of 10 is the most discriminatory, with over 2.7% better performance on the multiple-axis shapes in comparison with the conventional one. Additionally, the proposed detector also outperforms most of other existing methods. This finding should be recommended for applications in practice.

Pairing is carried out by two steps, Miller loop and final exponentiation. In this manuscript, the authors propose an efficient Miller loop for a pairing on the FK12 curve. A Hamming weight and bit-length of loop parameter have a great effect on the computational cost of Miller loop. Optimal-ate pairing is used as the most efficient pairing on the FK12 curve currently. The loop parameter of optimal-ate pairing is 6z+2 where z is the integer to make the FK12 curve parameter. Our method uses z which has a shorter bit-length than the previous optimal-ate pairing as the loop parameter. Usually, z has a low Hamming weight to make final exponentiation efficient. Therefore, the loop parameter in our method has a lower Hamming weight than the loop parameter of the previous one in many cases. The authors evaluate our method by the number of multiplications and execution time. As a result, the proposed algorithm leads to the 3.71% reduction in the number of multiplications and the 3.38% reduction in the execution time.

The suggested IsoQuadratic Exponentiation Randomized isocryptosystem design is the unique approach for public key encipher algorithm using IsoPartial Discrete Logarithm Problem and preservation of the recommended IsoQuadratic Exponentiation Randomized isocryptosystem be established against hardness of IsoPartial Discrete Logarithm Problem. Therewith, we demonstrated the possibility of an additional secured algorithm. The offered unique IsoQuadratic Exponentiation Randomized isocryptosystem is suitable for low bandwidth transmission, low storage and low numeration in cyberspace.

Modular exponentiation, especially for very large integers of hundreds or thousands of bits, is a commonly used function in popular cryptosystems such as RSA. The complexity of this algorithm is partly driven by the very large word sizes, which require many - often millions - of primitive operations in a CPU implementation, or a large amount of logic when accelerated by an ASIC. FPGAs, with their many embedded DSP resources have started to be used as well. In almost all cases, the calculations have required multiple - occasionally many - clock cycles to complete. Recently, blockchain algorithms have required very low-latency implementations of modular multiplications, motivating new implementations and approaches.In this paper we show nine different high performance modular exponentiation for 1024-bit operands, using a 1024-bit modular multiplication as it’s core. Rather than just showing a number of completed designs, our paper shows the evolution of architectures which lead to different resource mix options. This will allow the reader to apply the examples to different FPGA targets which may have differing ratios of logic, memory, and embedded DSP blocks. In one design, we show a 1024b modular multiplier requiring 83K ALMs and 2372 DSPs, with a delay of 21.21ns.

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer x to power k and reducing the result modulo some integer n. Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.

Pairings on elliptic curves are used for innovative protocols such as ID-based encryption and zk-SNARKs. To make the pairings secure, it is important to consider the STNFS which is the special number field sieve algorithm for discrete logarithms in the finite field. The Fotiadis-Konstantinou curve with embedding degree 12(FK12), is known as one of the STNFS secure curves. To an efficient pairing on the FK12 curve, there are several previous works that focus on final exponentiation. The one is based on lattice-based method to decompose the hard part of final exponentiation and addition chain. However, there is a possibility to construct a more efficient calculation algorithm by using the relations appeared in the decomposition calculation algorithm than that of the previous work. In this manuscript, the authors propose a relation of the decomposition and verify the effectiveness of the proposed method from the execution time.

We propose versatile hardware framework for ECC. The framework supports arithmetic operations over P-256, Ed25519 and Curve25519 curves, enabling easy implementation of various ECC algorithms. Framework finds its application area e.g. in FIDO2 attestation or in nowadays rapidly expanding field of hardware wallets. As the design is intended to be ASIC-ready, we designed it to be area efficient. Hardware units are reused for calculations in several finite fields, and some of them are superior to previously designed circuits in terms of time-area product. The framework implements several attack countermeasures. It enables implementation of certain countermeasures even in later stages of design. The design was validated on SoC FPGA.

Having high safety and effective computational property, the elliptic curve cryptosystem is very suitable for embedded mobile environment with resource constraints. Power attack is a powerful cipher attack method, it uses leaking information of cipher-chip in its operation process to attack chip cryptographic algorithms. In view of the situation that the power attack on the elliptic curve cryptosystem mainly concentrates on scalar multiplication operation an improved algorithm FWNAF based on RWNAF is proposed. This algorithm utilizes the fragments window technology further improves the utilization ratio of the storage resource and reduces the “jitter phenomenon” in system computing performance caused by the sharp change in system resources.

The concept of a connected vehicle refers to the linking of vehicles to each other and to other things. Today, developments in the Internet of Things (IoT) and 5G have made a significant contribution to connected vehicle technology. In addition to many positive contributions, connected vehicle technology also brings with it many security-related problems. In this study, a digital signature algorithm based on elliptic curve cryptography is proposed to verify the message and identity sent to the vehicles. In the proposed model, with the anonymous identification given to the vehicle by the central unit, the vehicle is prevented from being detected by other vehicles and third parties. Thus, even if the personal data produced in the vehicles is shared, it cannot be found which vehicle it belongs to.

Internet of Things (IoT) is widely present nowadays, from businesses to connected houses, and more. IoT is considered a part of the Internet of the future and will comprise billions of intelligent communication. These devices transmit data from sensors to entities like servers to perform suitable responses. The problem of securing these data from cyberattacks increases due to the sensitive information it contains. In addition, studies have shown that most of the time data transiting in IoT devices does not apply encrypted communication. Thus, anyone has the ability to listen to or modify the information. Encrypting communications seems mandatory to secure networks and data transiting from sensors to servers. In this paper, we propose an approach to secure the transmission and the storage of data in IoT using Elliptic Curve Cryptography (ECC). The proposed method offers a high level of security at a reasonable computational cost. Indeed, we present an adequate architecture that ensures the use of a state-of-the-art cryptography algorithm to encrypt sensitive data in IoT.

To contribute to medical data security, we propose the application of a modified algorithm on elliptical curves (ECC), initially proposed for text encryption. We implement this algorithm by eliminating the sender-receiver lookup table and grouping the pixel values into pairs to form points on a predefined elliptical curve. Simulation results show that the proposed algorithm offers the best compromise between the quality and the speed of cipher / decipher, especially for large images. A comparative study between ECC and AlGamel showed that the proposed algorithm offers better performance and its application, on medical images, is promising. Medical images contain many pieces of information and are often large. If the cryptographic operation is performed on every single pixel it will take more time. So, working on groups of pixels will be strongly recommended to save time and space.

Aim: Data is secured in the cloud using Elliptic Curve Cryptography (ECC) compared with Advanced Encryption Standard (AES) with improved time efficiency. Materials and Methods: Encryption and decryption time is performed with files stored in the cloud. Protecting data with improved time efficiency is carried out using ECC where the number of samples (\textbackslashmathrmN=6) and AES (\textbackslashmathrmN=6), obtained using the G-power value of 80%. Results: Mean time of ECC is 0.1683 and RSA is 0.7517. Significant value for the proposed system is 0.643 (\textbackslashmathrmp \textgreater 0.05). Conclusion: Within the limit of study, ECC performs faster in less consumption time when compared to AES.

Public key cryptography plays an important role in secure communications over insecure channels. Elliptic curve cryptography, as a variant of public key cryptography, has been extensively used in the last decades for such purposes. In this paper, we present a software tool for parallel generation of cryptographic keys based on elliptic curves. Binary method for point multiplication and C++ threads were used in parallel implementation, while secp256k1 elliptic curve was used for testing. Obtained results show speedup of 30% over the sequential solution for 8 threads. The results are briefly discussed in the paper.

Nowadays, cloud computing has become one of the most important and easily available storage options. This paper represents providing a platform where the data redundancy and the data security is maintained. Materials and Methods: This study contains two groups, the elliptic curve cryptography is developed in group 1 with 480 samples and advanced encryption is developed in group 2 with 960 samples. The accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Result: Advanced elliptic curve cryptography algorithm provides 1.2 times better performance compared to conventional elliptic curve cryptography algorithm for various datasets. The results were obtained with a significance value of 0.447 (p\textgreater0.05). Conclusion: From the obtained results the advanced elliptic curve cryptography algorithm seems to be better than the conventional algorithm.