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Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.

The integration of cloud computing and data security systems is vital for the operational success and competitiveness of fintech startups. Cloud computing enables these startups to scale quickly, manage resources efficiently, and reduce infrastructure costs, making it an indispensable tool for businesses in the rapidly evolving fintech sector. However, with the benefits come significant challenges, particularly in data protection and cybersecurity. As fintech services handle sensitive financial data, ensuring robust security measures such as encryption, access controls, and continuous monitoring is crucial to maintaining user trust. Furthermore, regulatory compliance, both local and global, adds complexity to the data protection strategies of fintech companies. This research explores the key factors that drive cloud adoption in fintech, the security challenges associated with cloud environments, and the strategies implemented by startups to address these challenges. Interviews with IT managers from Indonesian fintech startups reveal that while cloud computing offers scalability and cost-effectiveness, issues like compliance with local regulations and the protection of sensitive data remain major concerns. The research suggests that fintech startups should invest in both cloud infrastructure and advanced cybersecurity measures to protect their operations and customer data. Additionally, creating a comprehensive roadmap for regulatory compliance and fostering partnerships with cybersecurity firms will help mitigate risks and ensure long-term success. The findings highlight the importance of integrating cloud computing with effective security strategies to navigate the complex regulatory and security landscape of the fintech industry.

Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.

Data security in web services is a critical aspect that must be considered to prevent information leakage and unauthorized access. This study aims to analyze the security level of the Universitas Andalas Repository site, specifically regarding its vulnerability to sniffing attacks due to the use of the unencrypted HTTP protocol. The research employs a penetration testing method with an experimental approach. The testing process is carried out in three main stages: reconnaissance using Nmap to identify the services and protocols in use, sniffing using Wireshark to capture and analyze data packets, and evaluation of results based on modern web security standards to assess the level of risks found. The results of the study indicate that the Universitas Andalas Repository site still uses the HTTP protocol in its communication process, meaning that all data transmitted between the client and server can be read in plaintext. The Nmap scan results confirm that the web service operates on port 80 without SSL/TLS encryption support. Meanwhile, the packet analysis results using Wireshark show that both HTTP requests and responses can be captured and directly observed, including request parameters, cookies, and other potentially sensitive information. These findings suggest that the site has significant vulnerabilities to sniffing attacks and man-in-the-middle attacks, posing a potential risk to user data security.

This study aims to analyze how digitalization of governance, particularly through the implementation of e-financial reporting, digital audit trails, and cloud-based data security and reliability systems, contributes to increased transparency, accountability, and data protection in educational institutions. Using a qualitative approach with in-depth interviews and thematic analysis, this study reveals that digitalization can create a more objective, standardized, and difficult-to-manipulate control mechanism. The e-financial reporting system strengthens transparency through open processes and reduces opportunities for irregularities. Digital audit trails provide authentic evidence that facilitates the process of verification, monitoring, and tracking every administrative activity in real time. Meanwhile, data security supported by layered encryption, automatic backups, and emergency recovery access increases internal and external trust in the integrity of information systems. These findings indicate that digitalization not only provides technical tools but also forms a more disciplined, adaptive, and public accountability-oriented governance structure. This study broadens understanding of the relationship between digital technology and organizational culture and provides a theoretical foundation for developing a more reliable and sustainable information system-based educational governance model.

The exchange of information in the digital era has become a general need for society. However, the information sent often has a public or confidential nature. Therefore, security is needed so that confidential information remains safe. Cryptography is a field of knowledge used to secure information using encryption and decryption processes. One of the cryptographic methods used is the permutation method, which changes the layout, sequence, or structure of data into a form that is difficult to understand without knowledge of the exact key. Implementing cryptography using the permutation method in Android-based applications can increase the security and privacy of user data, as well as protect sensitive information from unauthorized access. This research aims to implement permutation method cryptography in Android-based applications to protect the confidentiality or privacy of user data. By using the permutation method, the sequence of bits or characters in the data is scrambled so that it is difficult for unauthorized parties to understand. The research results show that the implementation of permutation method cryptography in Android applications can provide a higher level of security in maintaining data confidentiality. However, it is worth considering that simple permutation methods may not be secure enough to deal with more sophisticated attacks. Therefore, choosing a stronger cryptographic method needs to be considered to achieve a higher level of security. In conclusion, implementing cryptography using the permutation method in Android-based applications can increase the security and privacy of user data. The permutation method is able to randomize the sequence of bits or characters in the data so that it is difficult for unauthorized parties to understand. However, for a higher level of security, it is necessary to consider stronger cryptographic methods.

The widespread use of digital images, driven by low-cost, handheld acquisition devices, has increased the need for robust security measures to safeguard privacy. This demand is further underscored by rising identity theft and other image-related crimes. This study presents a chaos-based experimental evaluation of contemporary image encryption algorithms. Owing to intrinsic properties such as sensitivity to initial conditions and pseudo-randomness, chaos theory has become increasingly prominent in image encryption. Five chaos-based image encryption schemes were selected and applied to a dataset of 26 color images. The evaluation covers both encryption performance and cryptographic security. Decryption quality is measured using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and DeepEns. Cryptographic security is assessed using entropy, correlation coefficient, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), average and maximum deviation, and histogram analysis. Experimental results indicate that all evaluated schemes demonstrate strong cryptographic security and comparable encryption performance, with broadly similar effectiveness across methods.

This systematic literature review examines the philosophy of science approaches to user security in distributed devices, such as IoT and Federated Learning. The review was conducted in response to the exponential growth of connected devices and the increasing security threats, including cyberattacks, data breaches, and unauthorized access. As distributed systems become more complex, traditional security approaches, such as cryptography and differential privacy, are often insufficient to address the ethical, philosophical, and contextual challenges that arise in these ecosystems. Distributed devices, especially in IoT and Federated Learning contexts, rely on vast amounts of personal data. This data, often stored or processed in decentralized environments, creates significant risks to user privacy and system integrity. As the number of connected devices grows, security risks multiply, creating challenges in maintaining user trust, privacy, and overall system resilience. Conventional techniques, such as encryption, only focus on technical aspects, often neglecting the deeper philosophical dimensions, such as the nature of knowledge, privacy, and fairness in these systems. These gaps highlight the need for a more nuanced approach that incorporates philosophical perspectives into security frameworks. This study uses a systematic literature review method based on the PICOC (Population, Intervention, Comparison, Outcome, Context) framework to analyze the relevance of epistemology, ontology, and ethics in strengthening system security. By examining the foundational principles of how knowledge is constructed (epistemology), what entities exist in the system (ontology), and the ethical considerations around data and user privacy (ethics), the review provides a comprehensive understanding of how philosophical concepts can be integrated into the design and implementation of security systems in distributed environments. The results reveal that epistemological principles, such as the verification and validation of data sources and models, can significantly improve the reliability and trustworthiness of distributed systems.

Rapid technological developments have penetrated various aspects of human life, including data and information security. Amidst the threat of data leaks and misuse, information protection has become crucial. One solution to maintain data confidentiality is to implement cryptography technology. Cryptography is the study of techniques to secure messages so they cannot be read by unauthorized parties. This process is carried out by encoding the original message (plain text) into an unreadable form (cipher text), which can only be understood by those who possess the key to decrypt it. This study used the Caesar Cipher algorithm, a form of classical cryptography. This algorithm uses a substitution method, where each letter in the message is shifted by a certain amount based on a predetermined key. Caesar Cipher is a symmetric algorithm, meaning the key used for encryption is also used for decryption. The security of this algorithm lies in the secrecy of the key, which is known only to the sender and recipient of the message. This method is relatively simple, but still relevant for securing data with low to medium sensitivity levels. This study used WhatsApp messages as the object to be encrypted using the Caesar Cipher algorithm. The results show that encrypted messages become unintelligible without prior decryption. This demonstrates that the Caesar Cipher can protect messages from unauthorized parties. Therefore, implementing this algorithm can be an initial solution for maintaining data confidentiality in digital communications, especially on platforms vulnerable to eavesdropping or unauthorized access. Its primary benefit is maintaining message integrity and confidentiality to prevent misuse.

This study explores the development and application of a digital twin-driven cybersecurity risk assessment model for Industrial Internet of Things (IIoT) networks. The increasing complexity and interconnectivity of IIoT systems have expanded the attack surface, making them vulnerable to a wide range of cyber threats. The digital twin model addresses this challenge by creating real-time virtual replicas of physical systems, which can simulate and predict network vulnerabilities and attack vectors. The model uses machine learning algorithms and real-time data to simulate cyberattacks, including Distributed Denial of Service (DDoS), malware, and data breaches. By providing continuous monitoring and dynamic risk predictions, the digital twin model enhances the resilience of IIoT networks compared to traditional cybersecurity frameworks. The findings indicate that the model's ability to predict potential cyber threats and simulate various attack scenarios provides a more proactive and accurate approach to cybersecurity in IIoT environments. Additionally, the study highlights key mitigation strategies, including adaptive security mechanisms, real-time anomaly detection, and the use of lightweight encryption for resource-constrained devices. Despite its effectiveness, challenges such as computational requirements, integration with legacy systems, and scalability were identified. This research underscores the strategic importance of digital twin models in securing IIoT systems and advancing Manufacturing 4.0 ecosystems. Future research should focus on enhancing model accuracy, expanding its application to diverse industrial sectors, and improving interoperability with legacy systems to further strengthen the security posture of IIoT networks.

Information technology (IT) has become a key element in the development of the financial industry, including Islamic banking. A primary challenge for IT management in Islamic banks is data security from cybercrime, which can damage the bank’s reputation and customer trust. This study aims to further examine the challenges and efforts that Islamic banks can undertake to enhance IT security. The research methodology uses a qualitative approach with descriptive methods and literature review to describe the phenomenon in depth based on data and information from various relevant literature sources. The study’s findings show that Islamic banks need to strengthen security through encryption, continuous monitoring, and employee training on cybersecurity. The conclusion of this study recommends that Islamic banks continue to raise awareness among employees and customers about security risks and ensure regulatory compliance. This research is expected to serve as a reference for Islamic banks in maintaining information security in the digital era.

As industries increasingly integrate cloud-based Industrial Control Systems (ICS), the cyber threat landscape expands. While cloud computing offers scalability, cost efficiency, and remote accessibility, it also introduces security vulnerabilities that adversaries can exploit. This study explores AI-driven threat detection models, encryption techniques, and best practices to enhance ICS resilience. Key security measures, including intrusion detection systems, anomaly detection, and robust encryption mechanisms, are analyzed to mitigate cyber risks. The findings highlight the effectiveness of AI-based security solutions in identifying and preventing attacks, ensuring the reliability and integrity of ICS in cloud environments.