Social Implications of Technological Advancements in Sentiment Analysis: A Literature Review on Potential and Consequences over the Next 20 Years by Daryanto, Ika Safitri Windiarti, Bagus Setya Rintyarna

“…It enables more responsive policy design by understanding public emotions in political and social contexts. However, data privacy, misinformation, and diminished critical thinking persist. …”
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Securing Decentralized Ecosystems: A Comprehensive Systematic Review of Blockchain Vulnerabilities, Attacks, and Countermeasures and Mitigation Strategies by Md Kamrul Siam, Bilash Saha, Md Mehedi Hasan, Md Jobair Hossain Faruk, Nafisa Anjum, Sharaban Tahora, Aiasha Siddika, Hossain Shahriar

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Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations by Peter Kováč, Peter Jackuliak, Alexandra Bražinová, Ivan Varga, Michal Aláč, Martin Smatana, Dušan Lovich, Andrej Thurzo

“…Current and future developments must focus on securing AI models against attacks, ensuring data integrity, and safeguarding the privacy of individuals within this technological landscape.…”
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Edge computing privacy protection method based on blockchain and federated learning by Chen FANG, Yuanbo GUO, Yifeng WANG, Yongjin HU, Jiali MA, Han ZHANG, Yangyang HU

“…Aiming at the needs of edge computing for data privacy, the correctness of calculation results and the auditability of data processing, a privacy protection method for edge computing based on blockchain and federated learning was proposed, which can realize collaborative training with multiple devices at the edge of the network without a trusted environment and special hardware facilities.The blockchain was used to endow the edge computing with features such as tamper-proof and resistance to single-point-of-failure attacks, and the gradient verification and incentive mechanism were incorporated into the consensus protocol to encourage more local devices to honestly contribute computing power and data to the federated learning.For the potential privacy leakage problems caused by sharing model parameters, an adaptive differential privacy mechanism was designed to protect parameter privacy while reducing the impact of noise on the model accuracy, and moments accountant was used to accurately track the privacy loss during the training process.Experimental results show that the proposed method can resist 30% of poisoning attacks, and can achieve privacy protection with high model accuracy, and is suitable for edge computing scenarios that require high level of security and accuracy.…”
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A location semantic privacy protection model based on spatial influence by Linghong Kuang, Wenlong Shi, Xueqi Chen, Jing Zhang, Huaxiong Liao

“…Nonetheless, while trajectory data mining enhances user convenience, it also exposes their privacy to potential breaches. …”
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A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs by Chengxue Zhang, Debin Kong, Peng Pan, Mingyuan Zhou

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Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory by Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota

“…In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. …”
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Children's digital privacy on fast-food and dine-in restaurant mobile applications. by Christine Mulligan, Grace Gillis, Lauren Remedios, Christopher Parsons, Laura Vergeer, Monique Potvin Kent

“…Restaurant mobile applications are powerful platforms for collecting users' data and are popular among children. This study aimed to provide insight into the privacy policies of top dine-in and fast-food mobile apps in Canada and data collected on child users. …”
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Privacy-enhanced federated learning scheme based on generative adversarial networks by Feng YU, Qingxin LIN, Hui LIN, Xiaoding WANG

“…Federated learning, a distributed machine learning paradigm, has gained a lot of attention due to its inherent privacy protection capability and heterogeneous collaboration.However, recent studies have revealed a potential privacy risk known as “gradient leakage”, where the gradients can be used to determine whether a data record with a specific property is included in another participant’s batch, thereby exposing the participant’s training data.Current privacy-enhanced federated learning methods may have drawbacks such as reduced accuracy, computational overhead, or new insecurity factors.To address this issue, a differential privacy-enhanced generative adversarial network model was proposed, which introduced an identifier into vanilla GAN, thus enabling the input data to be approached while satisfying differential privacy constraints.Then this model was applied to the federated learning framework, to improve the privacy protection capability without compromising model accuracy.The proposed method was verified through simulations under the client/server (C/S) federated learning architecture and was found to balance data privacy and practicality effectively compared with the DP-SGD method.Besides, the usability of the proposed model was theoretically analyzed under a peer-to-peer (P2P) architecture, and future research work was discussed.…”
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Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models by Baris Balaban, Seyma Selcan Magara, Caglar Yilgor, Altug Yucekul, Ibrahim Obeid, Javier Pizones, Frank Kleinstueck, Francisco Javier Sanchez Perez-Grueso, Ferran Pellise, Ahmet Alanay, Erkay Savas, Cetin Bagci, Osman Ugur Sezerman

“…Ensuring the security of both the model and the user data enables the protection of the intellectual property of ML models, preventing the leakage of sensitive information used in training and model users’ data.INDEX TERMS Homomorphic encryption, privacy-preserving machine learning, XGBoost.…”
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Blockchain-powered wireless sensor networks: enhancing security and privacy in the IoT era by Ibrahim Mekawy

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A Verifiable, Privacy-Preserving, and Poisoning Attack-Resilient Federated Learning Framework by Washington Enyinna Mbonu, Carsten Maple, Gregory Epiphaniou, Christo Panchev

“…Federated learning is the on-device, collaborative training of a global model that can be utilized to support the privacy preservation of participants’ local data. …”
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Privacy and Security in Digital Health Contact-Tracing: A Narrative Review by Shehani Pigera, Paul van Schaik, Karen Renaud, Miglena Campbell, Petra Manley, Pierre Esser

“…A total of 114 articles were retained as per the inclusion criteria, which included quantitative, qualitative, and mixed-methods studies. The data were analysed using thematic analysis. (3) Results: Eight main themes were derived: privacy, data protection and control, trust, technical issues, perceived benefit, knowledge and awareness, social influence, and psychological factors. (4) Conclusions: Improving privacy standards and the awareness of the digital contact-tracing process will encourage the acceptance of contact-tracing apps.…”
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Biometric-based medical watermarking system for verifying privacy and source authentication by Nada Fadhil Mohammed, Majid Jabbar Jawad, Suhad Ahmed Ali

“…Two of the most requirements in e-health care system is the ensuring the authenticity of the source from which the data is received and the privacy of medical record of the patient must be preserved. …”
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Digital citizenship literacy in Indonesia: The role of privacy awareness and social campaigns by Rossi Iskandar, Arifin Maksum, Arita Marini

“…A quantitative research approach was employed, using a survey method to collect data from 250 respondents of students from several high schools in Jakarta. …”
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Transparent and Privacy-Preserving Mobile Crowd-Sensing System with Truth Discovery by Ruijuan Jia, Juan Ma, Ziyin You, Mingyue Zhang

“…This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. …”
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An Exact Top- Query Algorithm with Privacy Protection in Wireless Sensor Networks by Huang Haiping, Feng Juan, Wang Ruchuan, Qin XiaoLin

“…The algorithm does the query exactly and meanwhile uses conic section privacy function to prevent the disclosure of the real data and then to promise the security of nodes in network. …”
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Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning by Raymond Jiang, Yulia Kumar, Dov Kruger

“…However, due to the many privacy regulations regarding personal data, pooling together data from multiple sources and storing them in a single (centralized) location for traditional ML model training are often infeasible. …”
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A multimodal differential privacy framework based on fusion representation learning by Chaoxin Cai, Yingpeng Sang, Hui Tian

“…Then based on this representation, we use the Local Differential Privacy (LDP) mechanism to protect data. …”
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Jointly Achieving Smart Homes Security and Privacy through Bidirectional Trust by Osman Abul, Melike Burakgazi Bilgen

“…Once approved, users are primarily concerned about privacy protection (i.e., user-to-system trust) when utilizing system services that require sensitive data for their functionality. …”
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