Post-Quantum AI: Building Secure Machine Learning Systems in the Quantum Era | Book Publisher International

 

This book explores the intersection of artificial intelligence (AI) and quantum computing, focusing on the urgent need to secure machine learning systems in the face of emerging quantum threats. As quantum computers advance, they expose vulnerabilities in classical cryptographic methods, potentially undermining data integrity, privacy, and trust in AI-driven applications. To address these challenges, this study introduces the concept of post-quantum AI—a framework for integrating quantum-resistant cryptographic algorithms, Anomaly detection mechanisms, and Resilient machine learning architectures. This book makes three core contributions: it motivates a quantum-era threat model for machine learning (ii) it maps a migration path to standardised post-quantum cryptography Crypto-agile architectures (iii) it presents a defence-in-depth blueprint across the data → training → inference lifecycle that integrates privacy-preserving learning Governance. This work explicitly highlights key contributions, including proposed frameworks, algorithms, and case studies. Future research directions are also outlined to guide continued exploration in this emergent field. The final candidate algorithms from the NIST PQC standardisation process (NIST, 2022–2023) further strengthen this discussion.

 

Key themes include the foundations of quantum mechanics relevant to computation, the fundamental differences between classical and quantum computing, and the transformative potential of quantum algorithms for optimisation, pattern recognition, and predictive analytics. The book highlights case studies spanning drug discovery, finance, mobile networks, and supply chain optimisation, illustrating how quantum-enhanced AI can revolutionise industry while simultaneously raising complex security and ethical concerns.

 

A central focus is the development and deployment of post-quantum cryptography (PQC)), such as lattice-based and hash-based algorithms, to safeguard AI models against adversarial and quantum-accelerated attacks. The discussion extends to adversarial machine learning, explainable AI (XAI), and hybrid classical–quantum systems as strategies for strengthening resilience.

 

The ethical, legal, and regulatory dimensions of post-quantum AI are also examined, emphasising fairness, transparency, accountability, and international cooperation. By combining technical innovations with responsible governance, the book advocates for building trustworthy AI systems that remain robust in the quantum era. Future work includes post-quantum cryptography (PQC) performance benchmarking in ML pipelines Patterns for crypto-agile key management, Assurance methods for privacy-preserving Federated learning as standards, and Implementations that are mature.

 

Author(s) Details

Amit Taneja
Vellore Institute of Technology, Tamil Nadu, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-88417-99-0

Real-Time FPGA Integration for Image Steganography Using Haar wavelet and Least Significant Bit Technique | Chapter 2 | Scientific Research, New Technologies and Applications Vol. 6

 

The purpose of this study is to develop a robust image steganography system that enhances the security of hidden communications by employing the Least Significant Bit (LSB) and Discrete Wavelet Transform (DWT) methods. The research aims to improve the robustness of the steganographic process and evaluate the effectiveness of these techniques. The study implements image steganography using two techniques: the Least Significant Bit (LSB) method and the Discrete Wavelet Transform (DWT) method. The performance of these algorithms is evaluated using key metrics such as Mean Squared Error (MSE), Bit Error Rate (BER), Peak Signal-to-Noise Ratio (PSNR), and processing time. The LSB method achieved PSNR values ranging from 42 to 46 dB and MSE values between 1.5 and 3.5. In contrast, the DWT method demonstrated superior performance, with PSNR values ranging from 49 to 57 dB and MSE values from 0.2 to 0.7. These results indicate that the DWT method provides higher performance and robustness compared to the LSB technique. The Discrete Wavelet Transform (DWT) method outperforms the Least Significant Bit (LSB) technique, offering better PSNR and lower MSE values. This makes DWT a more robust and efficient solution for image steganography, particularly in scenarios requiring high security and minimal image distortion.

 

 

Author(s) Details

Mangal Patil

Department of ECE, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.

Shankar Madkar

Department of ECE, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.

Jyoti Morbale

Department of ECE, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.

Harshal Hemane

Department of ECE, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v6/2197

 

A Comparative Review of Machine Learning and Computer Science Techniques for Optimising Healthcare Management Systems | Chapter 2 | New Horizons of Science, Technology and Culture Vol. 4

 

The application of computer science in the management of healthcare through Information technology is changing the ways medical services are being delivered with increased efficiency, quality, and positive health impacts to the patients. This study discusses the application of concepts from computer science in healthcare management systems and the strengthening of data security measures, the effectiveness of patient observation, and the development of recommendations for clinical practice. It highlights the role of advanced algorithms—such as the Harris Hawks Optimisation Algorithm—and emerging technologies like blockchain in facilitating more secure and efficient healthcare delivery. A comprehensive literature review was conducted using four academic online databases, which are PubMed, IEEE Xplore, ScienceDirect, and Google Scholar. Four algorithms were identified as particularly relevant to integrating computer science techniques in healthcare management systems, which include the Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Neural Networks. The results underline the importance of the focus on interdisciplinary strategies for solving modern healthcare issues. In addition to highlighting the importance of computer science innovations in the area of healthcare, this study offers suggestions for further improvements in patient outcomes. The findings of the study indicate that it is possible for the health care industry to foster these technologies for better and more efficient, secure and responsive, to the advantage of the patient as well as the health care provider. The study also calls for future research to explore novel methodologies for further advancing the application of computer science in healthcare management.

 

Author(s) Details

Deepak Sharma
Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, Raghogarh, Madhya Pradesh, India.

 

Jitendra Kanungo
Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, Raghogarh, Madhya Pradesh, India.

 

Narendra Singh
Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, Raghogarh, Madhya Pradesh, India.

 

Jitendra Raghuwanshi
Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, Raghogarh, Madhya Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v4/6011

GLObfus: An Innovative Method of Data Obfuscation to Protect Numerical Data in Public Cloud Storage | Chapter 4 | Mathematics and Computer Science: Contemporary Developments Vol. 4

Cloud computing has modified computing services with global convenience and accessibility. Many users undervalue the security risks associated with cloud data despite its integration into daily life. Data breaches can occur without user awareness, emphasizing the need for robust security measures. Cloud servers globally manage vast data volumes in real-time, posing ongoing security challenges. Protecting data from unauthorized access is of great necessity in cloud computing research. Numerical data security in the cloud can be enhanced by using GLObfus, an innovative method of data obfuscation. GLObfus transforms data into an unintelligible format using advanced mathematical calculations. Through this obfuscation method and different types of keys, GLObfus ensures that sensitive numerical data, such as student's marks and financial details, are rendered in an unintelligible format. This approach mitigates the risks of data exposure and unauthorized access by obscuring data before it is stored in the cloud. Operational efficiency and data security are significantly enhanced by GLObfus, which operates as a cloud utility within a Platform-as-a-service (PaaS) framework. Evaluations have demonstrated that GLObfus surpasses existing obfuscation techniques in terms of reusing time and increasing security measures. This makes it particularly well-suited for organizations that give priority to robust data protection in cloud environments, thereby establishing new security standards in the digital era.

 

Author (s) Details

D. I. George Amalarethinam
Jamal Mohamed College, Bharathidasan University, Tamil Nadu, India.

 

Lalu P. George
SCMS School of Technology and Management, Muttom, Alwaye, Cochin – 683 106, Kerala, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v4/1232

Logistics Management Security Practices: A Quantitative Analysis of Procedural Inventory Security Practices | Chapter 2 | Logistics Management Inventory Security Practices, Edition 1

Warehousing security is an emerging trend which is shaping the performance of logistics companies and ensuring a smooth supply chain flow. Several factors can interrupt the smooth flow of materials along the logistics cycle increasing risks and reducing customer satisfaction. To this end, this book chapter explores the effect of inventory security procedural practices on the security of inventory held by logistics service provider companies in Nairobi County. A descriptive research design was used as the research design. The target population of the study was composed of 583 employees of clearing and forwarding companies. The convenient sample size of the study was 175 employees of clearing and forwarding companies in Nairobi County, Kenya, which accounted for 30% of the target population. The convenient sampling method was used to target organizations with close proximity and convenience to the researcher saving time and money. Questionnaires were administered to warehouse employees for primary data collection. Simple frequency count and percentage for the demographic information and introduction sections of the study, followed by descriptive statistics and inferential statistics were used to analyze the data collected from the questionnaires. Descriptive statistics included mean, median and standard deviation. Inferential statistics included correlation and regression analysis which were done through SPSS. The book findings indicate that the results indicate a moderately strong relationship between the dependent variable (organizational logistical services performance) and the independent variable (inventory security procedural practices) by clearing and forwarding companies in Nairobi County (r = 0.47** p value = 0.000). This implies that the above relationship was significant because the p value 0.000 was less than 0.05 (significant level). Secondly, inventory security procedural practices have a significant difference in the overall mean of organizational logistical services performance (F = 4.87, p < 0.05). Therefore, inventory security procedural practices had a profound effect on the overall mean of organizational logistical services performance. A multiple coefficient of variation (R) value of 0.47 indicates a high and positive relationship between the dependent variable (organizational logistical services performance) and the independent variable (inventory security procedural practices). According to the R-Square value of 22 percent record-keeping practices, computerized stock documentation and stock marking activity explained 22 percent of variations in organizational logistical services performance by clearing and forwarding companies in Nairobi County. In the region of Kenya, random error or other factors account for 60.4 percent of those variations. In conclusion, as we move towards green suitable practices in the logistics sector, we should conduct further research on the potential contribution of environmental design activity. These green security initiatives can be used to reduce the environmental impact caused by other security measures that are more hazardous to the environment.

 

Author (s) Details

Lawrence Kabuthi Kabinga
Unicaf University, School of Business Old International Airport, Larnaca, Cyprus.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-35-0/CH2