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All Journal Jurnal Teknik Informatika C.I.T. Medicom
Lise Pujiastuti
STMIK Antar Bangsa, Tangerang, Banten, Indonesia
Computer Science & IT

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Journal : Jurnal Teknik Informatika C.I.T. Medicom

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Privacy-Preserving machine learning in edge computing environments Deni Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 3 (2023): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.621.pp118-125

Abstract

Edge computing has transformed data processing by moving computation closer to the source, enabling real-time analysis and decision-making. Edge devices are decentralized, which creates privacy and confidentiality concerns, especially when applying machine learning algorithms to sensitive data. Privacy-preserving machine learning methods for edge computing are examined in this research. Federated learning, homomorphic encryption, differential privacy, and secure aggregation are examined as data protection methods for network edge machine learning. A thorough study of these methods shows the challenges of balancing privacy, computational economy, and model correctness. Federated learning has promise for collaborative model training without raw data sharing, but communication overhead and convergence speed remain. A fictional healthcare use case shows how federated learning may be used to train collaborative models across many edge devices while protecting patient data. The case study stresses the necessity for sophisticated optimizations to overcome edge device limits and regulatory compliance. Federated learning algorithms, privacy-preserving procedures, and ethics must be improved, according to the research. Future directions include improving heterogeneous edge algorithms, addressing data ownership and consent ethics, and increasing model decision-making openness. This paper presents essential insights on privacy-preserving machine learning in edge computing and advocates for robust techniques for different edge environments. The paper emphasizes the importance of technological advances, ethical frameworks, and regulatory compliance for secure and privacy-aware machine learning in decentralized edge computing
Explainable artificial intelligence (XAI) for trustworthy decision-making Deni Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 5 (2023): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.622.pp240-246

Abstract

This research delves into the optimization of loan approval decisions by integrating the Trustworthy Decision Making (TDM) framework into a mathematical model. The study aims to strike a balance between maximizing loan approvals and ensuring fairness, transparency, and accountability in AI-driven decision-making processes. Leveraging principles of transparency, fairness, and accountability, the mathematical model seeks to optimize loan approvals while adhering to ethical considerations. The formulation emphasizes the importance of interpretable models to maintain transparency in decision explanations, ensuring alignment with trustworthy AI practices. Implementation results demonstrate the efficacy of the model in achieving a balanced approval rate across demographic groups while providing transparent explanations for decisions. This study highlights the significance of ethical considerations and mathematical formulations in fostering responsible AI implementations. However, continual refinement and adaptation of such models remain essential to align with evolving ethical standards and societal expectations. Overall, this research contributes to the discourse on responsible AI by showcasing a methodological approach that integrates ethical principles and mathematical formulations to promote fairness, transparency, and accountability in AI-driven decision-making.
Quantum computing in cryptography: Exploring vulnerabilities and countermeasures Deni Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 4 (2023): September : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.625.pp206-213

Abstract

This research delves into the critical analysis of vulnerabilities arising from the advent of quantum computing in traditional cryptographic systems. Employing a newly developed mathematical formulation model, the study meticulously evaluates the susceptibility of classical encryption methods, exemplified by XYZ Bank's RSA and ECC algorithms, to quantum algorithms such as Shor's and Grover's. The assessment reveals pronounced vulnerabilities, particularly highlighting the high susceptibility of RSA encryption to quantum attacks, emphasizing the urgent need to fortify existing cryptographic systems. The research rigorously evaluates potential countermeasures, with Post-Quantum Cryptography (PQC) emerging as a promising solution, showcasing superior effectiveness in mitigating vulnerabilities posed by quantum algorithms. The strategic imperative for organizations to transition towards PQC or other post-quantum cryptographic standards is evident, signaling a paradigm shift towards resilient encryption methods resilient to the disruptive capabilities of quantum computing. The research underscores the significance of collaboration among industry stakeholders, continuous research endeavors, and proactive measures in adopting quantum-resistant cryptographic standards to fortify data security strategies against potential quantum threats in an ever-evolving technological landscape.
Co-Authors Dedi Triyanto Deni Kurniawan Mochamad Wahyudi