Design and Implementation of an Ethical AI-Based Teaching Assistant for IoT Security Education by Matilda Isaacs, Anwar Majeed, Karim Moussa, Dolapo Shodipo

“…The system focused on ethical considerations such as data privacy, transparency, and accountability, fostering a learning environment where students can think critically, explore diverse perspectives, and engage meaningfully with AI technology. …”
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Enterprise internal audit data encryption based on blockchain technology. by Lixia Gao

“…Utilizing homomorphic Paillier encryption, BlockCryptoAudit ensures that computations may be performed on encrypted audit data while safeguarding data privacy. The applied blockchain hyperledger component guarantees the immutability and transparency of encrypted audit records, resulting in a decentralized and tamper-resistant record. …”
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Security Evaluation of Provably Secure ECC-Based Anonymous Authentication and Key Agreement Scheme for IoT by Kisung Park, Myeonghyun Kim, Youngho Park

“…Thus, robust, efficient authentication and key agreement (AKA) protocols are essential to protect data privacy during exchanges between end devices and servers. …”
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Adaptive selection method of desensitization algorithm based on privacy risk assessment by Lijun ZU, Yalin CAO, Xiaohua MEN, Zhihui LYU, Jiawei YE, Hongyi LI, Liang ZHANG

“…The financial industry deals with a vast amount of sensitive data in its business operations.However, the conventional approach of binding financial data for desensitization and using desensitization algorithms is becoming inefficient due to the fast-paced growth of financial businesses and the proliferation of data types.Additionally, manual verification and assessment of desensitized data by security experts are time-consuming and may carry potential privacy risks due to the improper selection of desensitization algorithms.While prior research has emphasized desensitization methods and privacy-preserving technologies, limited work has been conducted on desensitization algorithms from the perspective of automation.To address this issue, an adaptive recommendation framework was propose for selecting desensitization strategies that consider various factors, such as existing privacy protection technologies, data quality requirements of business scenarios, security risk requirements of financial institutions, and data attributes.Specifically, a dual-objective evaluation function was established for privacy risk and data quality to optimize the selection of desensitization algorithm parameters for different algorithms.Furthermore, the desensitization algorithm and parameters were adaptively selected by considering the data attributes through a multi-decision factor system and desensitization effect evaluation system.Compared to traditional approaches, the proposed framework effectively tackle issues of reduced data usability and inadequate personal data privacy protection that derive from manual intervention.Testing on a dataset with multiple financial institution types, the experiments show that the proposed method achieves a recommendation accuracy exceeding 95%, while the desensitized privacy risk level differed by less than 10% from the expected level.Additionally, the recommendation efficiency is 100 times faster than expert manual processing.…”
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Enhancing global model accuracy in federated learning with deep neuro-fuzzy clustering cyclic algorithm by Chin-Feng Lai, Ying-Hsun Lai, Ming-Chin Kao, Mu-Yen Chen

“…Therefore, the concept of federated learning was proposed in 2016, aiming to train models with different clients without sharing data to ensure data privacy. However, federated learning faces several challenges, including heterogeneous devices, data security, data heterogeneity, communication costs, and training time costs. …”
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Challenges of sharing individual participant data for secondary research on neglected tropical diseases: the experience of Drugs for Neglected Diseases initiative and a call for ac... by Anastassia Negrouk, Thaddaeus Egondi, Craig Tipple, Justyna Tarwid, Michelle Childs, Dominique Junod-Moser

“…It also aims to advance the debate about best practice in the research community to avoid ‘IPD sharing paralysis’, with a focus on multistakeholder projects involving patients and researchers based in countries with various levels of data privacy regulations and measures.Results The article describes a practical case study outlining the ethical, legal and technical challenges encountered by DNDi in the context of IPD data sharing. …”
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AI-Driven Sustainable Marketing in Gulf Cooperation Council Retail: Advancing SDGs Through Smart Channels by Hanadi Salhab, Munif Zoubi, Laith T. Khrais, Huda Estaitia, Lana Harb, Almotasem Al Huniti, Amer Morshed

“…While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. …”
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From data to diagnosis: leveraging deep learning in IoT-based healthcare by Miracle A. Atianashie, Chukwuma Chinaza Adaobi

“…Despite these advancements, challenges remain such as data privacy, interoperability of diverse systems, and the computational demands of processing large-scale data. …”
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DIGITALIZATION AND ESG SYNERGIES. TRANSFORMING REGIONAL DEVELOPMENT THROUGH CORPORATE ACTIONS by SPULBAR LUCIAN FLORIN, MITRACHE DANIEL MARIUS, MITRACHE LAVINIA ADELINA

“…This article delves into potential risks, such as data privacy issues, digital divides, and the need for stronger regulatory frameworks to guide corporate behavior. …”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…However, several challenges, including the availability of relevant, labeled, high‐quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. …”
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Survey on blockchain privacy protection techniques in cryptography by Feng LIU, Jie YANG, Jiayin QI

“…In recent years, the issue of data privacy has attracted increased attention, and how to achieve effective privacy protection in blockchain is a new research hotspot.In view of the current research status and development trend of blockchain in privacy protection, the privacy protection methods of blockchain in transaction address,prophecy machine and smart contract were explained, and the privacy strategies of blockchain in the protection of basic elements were summarized.Based on high-level literature at home and abroad, two types of blockchain cryptographic protection methods and usage scenarios were analyzed, including special cryptographic primitives and post-quantum cryptography.The advantages and disadvantages of seven cryptographic techniques applicable to current blockchain privacy protection were also reviewed, including attribute-based encryption, special data signature, homomorphic encryption, secure multi-party computation, zero-knowledge proofs, and lattice ciphers.It was concluded that the privacy protection of blockchain applications cannot be achieved without cryptographic technology.Meanwhile, the blockchain privacy protection technologies were analyzed in terms of both basic element protection and cryptographic protection.It was concluded that it was difficult to effectively solve the privacy problem only from the application and contract layers of the blockchain, and various cryptographic technologies should be used to complement each other according to different needs and application scenarios.In addition, according to the current development status of blockchain privacy cryptography, the narrative was developed from blockchain basic element protection and cryptography-based protection.From the perspectives of both endogenous basic element security and exogenous cryptographic privacy security, basic element privacy protection should be studied first, followed by an in-depth analysis of cryptographic protection techniques for blockchain privacy.The strengths and weaknesses and the potential value of the privacy handling aspects of the corresponding safeguards should be measured in terms of the development of technology in conjunction with practical applications, while considering the timeliness of the technology.Finally, an outlook on the future direction of blockchain privacy protection technologies was provided, indicating the issues that need to be addressed in focus.…”
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Federated Learning Based on Kernel Local Differential Privacy and Low Gradient Sampling by Yi Chen, Dan Chen, Niansheng Tang

“…By combining RFFM-KLDP and modified low-gradient sampling technique, we develop a novel and robust federated learning method for classification in the presence of the noisy text data, which can preserve data privacy and largely improve the accuracy of classification algorithm compared to the existing classifiers in terms of the area under curve and classification accuracy. …”
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Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…Traditional centralized deep learning models have shown promising results, but they raise concerns related to data privacy, as data needed to be collected and trained on a single node. …”
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Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications by Saeed Hamood Alsamhi, Ammar Hawbani, Alexey V. Shvetsov, Santosh Kumar

“…Specifically, FL offers the potential to revolutionize pandemic preparedness within Healthcare 5.0 in several vital ways: it enables collaborative learning from distributed data sources without compromising individual data privacy, facilitates decentralized decision-making by empowering local healthcare institutions to contribute to a collective knowledge pool, and enhances real-time surveillance, enabling early detection of outbreaks and informed responses. …”
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The Role of ChatGPT and AI Chatbots in Optimizing Antibiotic Therapy: A Comprehensive Narrative Review by Ninel Iacobus Antonie, Gina Gheorghe, Vlad Alexandru Ionescu, Loredana-Crista Tiucă, Camelia Cristina Diaconu

“…However, challenges such as inconsistent handling of clinical nuances, susceptibility to unsafe advice, algorithmic biases, data privacy concerns, and limited clinical validation underscore the importance of human oversight and refinement. …”
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Neuroethical issues in adopting brain imaging for personalized chronic pain management: Attitudes of people with lived experience of chronic pain by Karen Deborah Davis, Monica de Oliveira, Ariana Besik, Daniel Z. Buchman

“…Brain imaging technologies can potentially inform pain management but raise neuroethical questions.Aims We examined the degree of endorsement and concerns of adults in Canada with chronic pain regarding the use of brain imaging to detect and treat chronic pain in six areas: new brain imaging technologies, brain data privacy, stigma, treatment, objective representations of pain, and dismissing pain self-reports.Results An online survey was completed by 349 Canadian adults living with chronic pain. …”
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Data Security in Autonomous Driving: Multifaceted Challenges of Technology, Law, and Social Ethics by Yao Xu, Jixin Wei, Ting Mi, Zhihua Chen

“…(a) Technologically, issues such as data leakage, storage vulnerabilities, and the risk of re-identifying anonymous data persist; (b) legally, there is an urgent need to clarify the responsible parties and address issues related to outdated data security legislation and legal conflicts arising from cross-border data flows; (c) socially and ethically, the risks of data misuse and the emergence of exploitative contracts have triggered public concerns about data privacy. To address these challenges, this article proposes technical countermeasures such as utilizing diverse Privacy Enhancing Technologies (PETs) to enhance data anonymity, optimizing data encryption techniques, and reinforcing data monitoring and access control management. …”
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Using artificial intelligence based language interpretation in non-urgent paediatric emergency consultations: a clinical performance test and legal evaluation by Julia Brandenberger, Ian Stedman, Noah Stancati, Karen Sappleton, Sarathy Kanathasan, Jabeen Fayyaz, Devin Singh

“…Legal evaluation indicated inconsistent access to language interpretation services across healthcare jurisdictions and potential risks involving data privacy, consent, and malpractice when using AI-based translation tools. …”
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Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approach by Junyoung Byun, Jaewook Lee, Hyeongyeong Lee, Bumho Son

“…Predicting bank failures is a critical task requiring balancing the need for model explainability with the necessity of preserving data privacy. Traditional machine learning models often lack transparency, which poses challenges for stakeholders who need to understand the factors leading to predictions. …”
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Integrating AI and statistical methods for enhancing civil structural practices: current trends, practical issues, and future direction by Asraar Anjum, Meftah Hrairi, Abdul Aabid Shaikh, Noorfazrina Yatim, Maisarah Ali

“…The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. …”
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