Artificial Intelligence: An Untapped Opportunity for Equity and Access in STEM Education by Shalece Kohnke, Tiffanie Zaugg

“…., unequal representation in training datasets leading to unfair assessments) and data privacy risks (e.g., potential breaches of sensitive student data), require critical attention to ensure AI systems promote equity rather than exacerbate disparities. …”
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Big Data Competition and Market Power by Marco Gambaro

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Convergence of nanotechnology and artificial intelligence in the fight against liver cancer: a comprehensive review by Manjusha Bhange, Darshan Telange

“…This convergence holds significant promise for transforming liver cancer therapy into a more precise, individualized, and efficient process. However, data privacy, regulatory hurdles, and the need for large-scale clinical validation remain. …”
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Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review by Blanka Klimova, Marcel Pikhart

“…Furthermore, issues such as data privacy and job displacement emerge as AI technologies permeate educational environments. …”
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Double layer federated security learning architecture for artificial intelligence of things by ZHENG Chengbo, YAN Haonan, FU Caili, ZHANG Dong, LI Hui, WANG Bin

“…Federated learning, as a distributed machine learning architecture, can complete model co-training while protecting data privacy, and is widely used in Artificial Intelligence of Things. …”
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Detecting privacy compliance of mobile applications from the perspective of the "minimum necessary" principle by YU Peihou, XU Tianchen, SUN Wenqian, CHEN Yunfang, YU Le, ZHANG Wei

“…To comply with legal requirements for personal data privacy protection, mobile App developers typically disclose their data collection practices to users through privacy policies. …”
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Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning by Ahmad Aldelemy, Raed A. Abd-Alhameed

“…The workflow is simple, adaptable, and suitable for UK banks, demonstrating the potential for practical implementation and data privacy. Future work will extend our approach to UK banks, reformulate the problem as a multi-class classification, and introduce pre-training automated steps for data analysis and transformation. …”
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Revolutionizing urban solid waste management with AI and IoT: A review of smart solutions for waste collection, sorting, and recycling by Abderrahim Lakhouit

“…The review also addresses challenges such as high initial costs, privacy concerns, and technical limitations, advocating for continued investment in innovative technologies and robust data privacy solutions. Ultimately, this research promotes a holistic approach to sustainable waste management that centers on building a circular economy and reducing environmental impacts. …”
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Promoting Digital Health Data Literacy: The Datum Project by Daniel Powell, Laiba Asad, Elissa Zavaglia, Manuela Ferrari

“…The Datum project, funded by the Fondation Barreau du Quebec, was created to help these actors better understand legal and ethical issues regarding the collection, use, and disclosure of digital health data for the purposes of scientific research, thereby enhancing literacy around data privacy. The project consists of a multimedia website divided into legislation and policy documents and narrative-based video content. …”
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Blockchain-Enabled Zero Trust Architecture for Privacy-Preserving Cybersecurity in IoT Environments by Mohammed A. Aleisa

“…This architecture entails data privacy and confidentiality, auditability and traceability, and withstanding evolving threats, including potential threats in terms of quantum attacks. …”
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Filipino attitude and perception on Subscriber Identity Module (SIM) Card Registration Act by Aera L. Diaz, Romano M. Balbacal

“…Participants’ attitudes toward the Act were shaped by their experiences with mobile phone technology and privacy concerns, with older respondents expressing heightened sensitivity to data privacy. These insights underscore the need for more robust security measures in the registration process to enhance public trust. …”
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Searchable encryption scheme based on attribute policy hiding in a cloud environment by Yihua ZHOU, Xinyu HU, Meiqi LI, Yuguang YANG

“…Attribute-based searchable encryption technology can achieve fine-grained access control of data, but the existing searchable encryption scheme, keyword search, access control and file encryption are basically performed separately, causing the attacker to directly skip the access policy for keyword index matching and file decryption.Besides, the data owners in the existing schemes need to pass the key of the encrypted file to the user in a secure channel, which increases the cost of the data owner.Furthermore, most tree-based access control policies are open and easy to cause privacy leakage.Therefore, based on the LSSS (linear secret sharing schemes) access architecture, the searchable encryption scheme based on attribute policy hiding in a cloud environment was proposed.Through the embedding of policy secret values into keyword encryption and file storage encryption, the combination of access control, keyword search and file encryption were realized.The aggregate key technology enables users to decrypt files without interacting with the data owner, reducing the burden of key management and increasing storage space by approximately 30%.The experimental results and security analysis show that the proposed scheme guarantees the security of stored data, privacy of access strategy and non-connectivity of trap gate.Compared with the existing mainstream scheme, the retrieval efficiency of the proposed scheme has improved to more than 20%.…”
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A Secure Data Sharing Model Utilizing Attribute-Based Signcryption in Blockchain Technology by Chaoyue Song, Lifeng Chen, Xuguang Wu, Yu Li

“…However, this also poses challenges related to data privacy breaches and holdership threats. Typically, blockchain technology and cloud storage provide effective solutions. …”
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The Impact of Artificial Intelligence, Ethical Implications and Technologies on the Electoral Process by Itumeleng Michael Maine, Bukohwo Michael Esiefarienrhe

“…Also discussed are the various ways that AI can be used negatively to influence voters namely deepfakes, automated bots, data privacy breaches, microtargeting, psychological profiles of voters, voting pattern prediction, and cyberattacks of electronic devices with the intention to rig elections. …”
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Life Cycle Analysis in the Context of Smart Cities by Berville Charles, Croitoru Cristiana, Bode Florin

“…However, challenges such as the high energy use of ICT infrastructure, electronic waste, construction impacts, data privacy, cybersecurity risks and the digital divide are significant. …”
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Privacy-preserving federated learning framework with dynamic weight aggregation by Zuobin YING, Yichen FANG, Yiwen ZHANG

“…There are two problems with the privacy-preserving federal learning framework under an unreliable central server.① A fixed weight, typically the size of each participant’s dataset, is used when aggregating distributed learning models on the central server.However, different participants have non-independent and homogeneously distributed data, then setting fixed aggregation weights would prevent the global model from achieving optimal utility.② Existing frameworks are built on the assumption that the central server is honest, and do not consider the problem of data privacy leakage of participants due to the untrustworthiness of the central server.To address the above issues, based on the popular DP-FedAvg algorithm, a privacy-preserving federated learning DP-DFL algorithm for dynamic weight aggregation under a non-trusted central server was proposed which set a dynamic model aggregation weight.The proposed algorithm learned the model aggregation weight in federated learning directly from the data of different participants, and thus it is applicable to non-independent homogeneously distributed data environment.In addition, the privacy of model parameters was protected using noise in the local model privacy protection phase, which satisfied the untrustworthy central server setting and thus reduced the risk of privacy leakage in the upload of model parameters from local participants.Experiments on dataset CIFAR-10 demonstrate that the DP-DFL algorithm not only provides local privacy guarantees, but also achieves higher accuracy rates with an average accuracy improvement of 2.09% compared to the DP-FedAvg algorithm models.…”
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Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review by Jacqueline H Stephens, Celine Northcott, Brianna F Poirier, Trent Lewis

“…The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged. …”
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The Comparison of Indonesian and American Consumer Protection Laws: What and How? by Dwi Edi Wibowo

“…Indonesia relies on the Consumer Protection Act with a focus on basic consumer rights and non-litigation dispute resolution, while the US combines common law with federal and state laws, providing broader protection including data privacy and product safety. The implementation of laws in both countries also differs, with Indonesia prioritizing non-litigation mediation through BPSK (Consumer Dispute Settlement Body), while the US has a strong litigation system including class action mechanisms. …”
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