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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Democratic Oversight of Government Hacking by Intelligence Agencies by André Ramiro

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Social Media Suicide Watch by Katherine Prothro

“…There was an immediate backlash due to concerns over privacy and the potential for stalkers and bullies to misuse this data and encourage suicide or self-harm, like Roy’s girlfriend did. …”
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Optimization of machine learning methods for de-anonymization in social networks by Nurzhigit Smailov, Fatima Uralova, Rashida Kadyrova, Raiymbek Magazov, Akezhan Sabibolda

“…Anonymity features are widely used to help individuals maintain their privacy, but they can also be exploited for malicious purposes. …”
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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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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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Blockchain-powered wireless sensor networks: enhancing security and privacy in the IoT era by Ibrahim Mekawy

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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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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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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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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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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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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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Challenges in IoMT Adoption in Healthcare: Focus on Ethics, Security, and Privacy by Alton Mabina, Neo Rafifing, Boago Seropola, Thapelo Monageng, Pulafela Majoo

“…This study highlights ethical, security, and privacy barriers to IoMT adoption in developing countries and proposes strategies like regulatory frameworks, data encryption, AI transparency, and professional training to address these challenges. …”
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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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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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A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs by Chengxue Zhang, Debin Kong, Peng Pan, Mingyuan Zhou

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PRIVocular: Enhancing User Privacy Through Air-Gapped Communication Channels by Anastasios N. Bikos

“…PRIVocular (i.e., PRIV(acy)-ocular) is a VR-ready hardware–software integrated system that is capable of visually transmitting user data over three versatile modes of encapsulation, encrypted—without loss of generality—using an asymmetric-key cryptosystem. …”
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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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Location Privacy-Preserving Channel Allocation Scheme in Cognitive Radio Networks by Hongning Li, Qingqi Pei, Wenjing Zhang

“…In this paper, to make full use of idle spectrum with low probability of location leakage, we propose a Location Privacy-Preserving Channel Allocation (LP-p CA) scheme. …”
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