STF-LPPVA: Local Privacy-Preserving Method for Vehicle Assignment Based on Spatial–Temporal Fusion by Lei Tang, Zhengxin Cao, Xin Zhou, Junzhe Zhang, Junchi Ma

“…There are user privacy risks in cloud-based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. …”
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Recent Advances in Federated Learning for Connected Autonomous Vehicles: Addressing Privacy, Performance, and Scalability Challenges by Asad Ali, Huang Jianjun, Ayesha Jabbar

“…FL presents a decentralized infrastructure that allows collaborative learning, while also ensuring data privacy, as CAVs increasingly rely on machine learning to process large amounts of sensor data. …”
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Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) by Komal Goyal, Megha Garg, Shruti Malik

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A Reliable Application of MPC for Securing the Tri-Training Algorithm by Hendra Kurniawan, Masahiro Mambo

“…Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. …”
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Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks by Liang Liu, Zhenhai Hu, Lisong Wang

“…The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. …”
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Blockchain-Enabled Federated Learning to Enhance Security and Privacy in Internet of Medical Things (IoMT) by zahra eskandari

“…Federated learning is a distributed data analysis approach used in many IoT applications, including IoMT, due to its ability to provide acceptable accuracy and privacy. …”
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A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices by Katsuhiro Honda, Toshiya Oda, Daiji Tanaka, Akira Notsu

“…In many real world data analysis tasks, it is expected that we can get much more useful knowledge by utilizing multiple databases stored in different organizations, such as cooperation groups, state organs, and allied countries. …”
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Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing by Nada Alasbali, Jawad Ahmad, Ali Akbar Siddique, Oumaima Saidani, Alanoud Al Mazroa, Asif Raza, Rahmat Ullah, Muhammad Shahbaz Khan

“…Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training.MethodsKeeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. …”
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Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

“…These methods work with data locality during training at local clients without exposing data, while maintaining global convergence to enhance the privacy of local models within the framework. …”
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Shielding Communication Privacy: Unveiling The Strategic Utilization Of Instagram’s Second Account Feature By Millennial Generation by Musfiah Saidah

“…Even though the person who is selected to enter the second account circle is also vulnerable to opening the data privacy of the account owner and even spreading it. …”
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Improved Denclue Outlier Detection Algorithm With Differential Privacy and Attribute Fuzzy Priority Relation Ordering by Huangzhi Xia, Limin Chen, Dongyan Wang, Xiaotong Lu

“…Firstly, the differential privacy technology is used to add the Laplacian noise to the density to realize the sensitive information hiding among the data objects. …”
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How Service Quality and Perceived Privacy Can Affect the Customer Satisfaction in Generations Y and Z? by M Iqbal Nurfaizi, Endy Gunanto Marsasi

“…Data were collected by distributing questionnaires using the purposive sampling method to 190 generation Y and Z participants who used online food delivery. …”
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Explainable Federated Framework for Enhanced Security and Privacy in Connected Vehicles Against Advanced Persistent Threats by Sudhina Kumar G K, Krishna Prakasha K, Balachandra Muniyal, Muttukrishnan Rajarajan

“…The critical need for vehicular data privacy restricts traditional centralized Machine Learning (ML) approaches. …”
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An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments by Tong Niu, Yaqiu Liu, Qingfeng Li, Qichi Bao

“…The model is then trained using federated learning/edge computing to ensure data privacy. …”
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Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT by Amelia Ritahani Ismail, Faris Farhan Azlan, Khairul Akmal Noormaizan, Nurul Afiqa, Syed Qamrun Nisa, Ahmad Badaruddin Ghazali, Andri Pranolo, Shoffan Saifullah

“…To safeguard sensitive information, Differential Privacy Stochastic Gradient Descent (DP-SGD) is integrated using TensorFlow-Privacy, achieving a privacy budget of ε ≈ 1.5 with minimal performance degradation. …”
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Shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means by Suxia ZHU, Shulun LIU, Guanglu SUN

“…To solve the problem that most studies had not fully considered the sensitivity of location to privacy budget and the influence of trajectory shape, which made the usability of published trajectory poor, a shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means was proposed.Firstly, according to the topological relationship of geographic space, relative entropy was used to calculate the sensitivity of real location to privacy budget, a real-time calculation method of location sensitive privacy level was designed, and a new privacy model was built in combination with differential privacy budget.Secondly, K-means algorithm was used to cluster the release position to obtain the release position set that was most similar to the real position direction, and Fréchet distance was introduced to measure the similarity between the release track and the real track, so as to improve the availability of the release track.Experiments on real data sets show that the proposed trajectory protection mechanism has obvious advantages in trajectory availability compared with others.…”
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Sharenting in Asunción, Paraguay: Parental Behavior, Risk Perception, and Child Privacy Awareness on Social Media by María Nieto-Sobrino, Nidia Beatriz Pérez Maciel, María Sánchez-Jiménez

“…A survey of 73 parents analysed posting habits, knowledge of risks, and possible influencing factors on parental digital behaviour. Data analysis techniques such as descriptive statistics and correlation analysis were used to examine the associations between the key variables. …”
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PP-PQB: Privacy-Preserving in Post-Quantum Blockchain-Based Systems: A Systematization of Knowledge by Bora Bugra Sezer, Sedat Akleylek, Urfat Nuriyev

“…To the best of our knowledge, there is no comprehensive review or taxonomy that provides a complete picture of post-quantum secure structures with privacy-preserving techniques that have the potential to be used in blockchain. …”
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Physically secure and fog-enabled lightweight authentication scheme for WBAN by Jegadeesan Subramani, Arun Sekar Rajasekaran, Arunkumar Balakrishnan, G. Anantha Rao

“…Abstract Wireless Body Area Networks (WBANs) are vital for healthcare, fitness monitoring, and remote patient care by means of combining sensors and wearable technologies for data collection and transmission. However, ensuring secure communication in WBANs remains a critical challenge and is generally insecure against the manipulation of data, breaches of privacy, and unauthorized access. …”
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Goalie: Defending Against Correlated Value and Sign Encoding Attacks by Rongfei Zhuang, Ximing Fu, Chuanyi Liu, Peiyi Han, Shaoming Duan

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