The digital fingerprint of learner behavior: Empirical evidence for individuality in learning using deep learning by Asaf Salman, Giora Alexandron

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A novel federated learning approach for IoT botnet intrusion detection using SHAP-based knowledge distillation by Md. Alamgir Hossain, Sadman Saif, Md. Saiful Islam

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Does data privacy influence digital marketing? The mediating role of AI-driven trust: An empirical study of Zain Telecom company in Jordan by Nidal Al Said

“…The PLS-SEM technique was used in this research to analyze data privacy, digital marketing effectiveness, and AI-driven trust constructs. …”
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Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection by Mahesh Vaijainthymala Krishnamoorthy

“… Abstract BackgroundThe increasing integration of artificial intelligence (AI) systems into critical societal sectors has created an urgent demand for robust privacy-preserving methods. Traditional approaches such as differential privacy and homomorphic encryption often struggle to maintain an effective balance between protecting sensitive information and preserving data utility for AI applications. …”
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Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks by Aysha-Thahsin Zahir-Ismail, Raj Shukla

“…Abstract Distributed Collaborative Machine Learning (DCML) offers a promising alternative to address privacy concerns in centralized machine learning. …”
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FedSVD: Asynchronous Federated Learning With Stale Weight Vector Decomposition by Giwon Sur, Hyejin Kim, Seunghyun Yoon, Hyuk Lim

“…Federated learning (FL) emerges as a collaborative learning framework that addresses the critical needs for privacy preservation and communication efficiency. …”
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Automation of Daily Access Data Recap of Employee Data Tables Using Apps Script to Detect Illegal Activities: by Jong Jek Siang, Rio Rian Rivanka, Argo Wibowo

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A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference by Sadhana Selvakumar, B. Senthilkumar

“…However, sharing sensitive raw medical data with third parties for analysis raises significant privacy concerns. …”
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Blockchain oracles for decentralized agricultural insurance using trusted IoT data by Manoj T, Krishnamoorthi Makkithaya, Narendra V. G., Vijaya Murari T

“…Initially, a method for computing the direct reputation score of IoT devices based on behavioral and data reputation is illustrated. Next, a privacy preserved decentralized oracle mechanism is designed and implemented using a masked secret sharing and secure aggregation scheme. …”
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Convolutional neural network model over encrypted data based on functional encryption by Chen WANG, Jiarun LI, Jian XU

“…Currently, homomorphic encryption, secure multi-party computation, and other encryption schemes are used to protect the privacy of sensitive data in outsourced convolutional neural network (CNN) models.However, the computational and communication overhead caused by the above schemes would reduce system efficiency.Based on the low cost of functional encryption, a new convolutional neural network model over encrypted data was constructed using functional encryption.Firstly, two algorithms based on functional encryption were designed, including inner product functional encryption and basic operation functional encryption algorithms to implement basic operations such as inner product, multiplication, and subtraction over encrypted data, reducing computational and communication costs.Secondly, a secure convolutional computation protocol and a secure loss optimization protocol were designed for each of these basic operations, which achieved ciphertext forward propagation in the convolutional layer and ciphertext backward propagation in the output layer.Finally, a secure training and classification method for the model was provided by the above secure protocols in a module-composable way, which could simultaneously protect the confidentiality of user data as well as data labels.Theoretical analysis and experimental results indicate that the proposed model can achieve CNN training and classification over encrypted data while ensuring accuracy and security.…”
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Data Security Model Using (AES-LEA) Algorithms for WoT Environment by Zinah A. Al-jazaeri, Joolan Rokan Naif, Ahmad Mohamad Ghandour

“…Therefore, ensuring data privacy and protection is a major challenge for organizations and individuals. …”
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Social Engineering Threat Analysis Using Large-Scale Synthetic Data by Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Pulasthi Gunawardhana

“…Our model achieved an accuracy of 0.8984 and an F1 score of 0.9253, demonstrating its effectiveness in detecting social engineering attacks. The use of synthetic data overcomes the problem of lack of availability of real-world data due to privacy issues, and is demonstrated in this work to be safe, scalable, ethics friendly and effective.…”
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Approach to keyword search over encrypted data in cloud by Peng ZHANG, Yan LI, Hai-lun LIN, Rong YANG, Qing-yun LIU

“…With the advent of cloud computing,large-scale data are being increasingly outsourced to the cloud.For the protection of data privacy,sensitive data has to be encrypted before outsourcing,which makes effective data utilization a very challenging task.Although traditional searchable encryption approaches allow users to search over encrypted data through keywords,they don’t capture any relevance of data files,so users have to spend much time on post-processing every retrieved file in order to find ones most matching their interest.Moreover,retrieving all files containing the queried keyword further incurs unnecessary network traffic,which is not accord with pay-as-you-use cloud paradigm.An approach to ranked keyword search over encrypted data in cloud is proposed.Ranked search greatly mitigate the user’s effort by returning the matching files in a ranked order,and also protects the data privacy by order-preserving encryption.Extensive experimental results demonstrate the efficiency.…”
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Collection of location data in criminal proceedings – European (the EU and Strasbourg) standards by Dominika Czerniak

“…This article deals with the problem of collecting, retaining and processing location data for use in criminal proceedings. The collection of location data is an interference with the right to privacy (the Article 8 of the ECHR, the Article 7 of the Charter). …”
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How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data by Gabriele Santangelo, Giovanna Nicora, Riccardo Bellazzi, Arianna Dagliati

“…Conclusions Synthetic data mitigate concerns about privacy and data accessibility, yet lacks standardized evaluation metrics. …”
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Secure and trusted sharing mechanism of private data for Internet of Things by Mengyuan Li, Shaoyong Guo, Wenjing Li, Ao Xiong, Xiaoming Zhou, Jun Qi, Feng Qi, Dong Wang, Da Li

“…This practice creates the risk of privacy breaches on IoT data sharing platforms, including issues such as data tampering and data breaches. …”
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Tabular Data Augmentation Using Artificial Intelligence: A Systematic Review and Taxonomic Framework by Mauro Henrique Lima de Boni, Iwens Gervasio Sene Junior, Ronaldo Martins da Costa

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The tension of usable safety, security and privacy by Kaufhold Marc-André, Mentler Tilo, Nestler Simon, Reuter Christian

“…In this special issue, we investigate the use of computer-based solutions in areas and situations of direct relevance to people’s lives and well-being (Usable Safety), as well as contributions to user-oriented resilience concepts of sociotechnical systems concerning potential attacks (Usable Security) and data protection mechanisms (Usable Privacy).…”
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Survey on privacy protection indoor positioning by Zhiheng WANG, Yanyan XU

“…Smartphones are usually provided with indoor positioning services by third-party positioning service providers, in which the unique privacy leakage risk has become a major factor limiting its development.How to protect the privacy of users and data in the positioning process has become an important issue to be solved.The research progress of indoor positioning privacy protection in recent years was reviewed.The commonly used indoor positioning technologies were introduced, different implementation architectures of indoor positioning systems and their threat models, privacy protection requirements were discussed, security technologies applied to indoor positioning privacy protection were summarized, indoor positioning privacy protection schemes for different architectures were classified and introduced, and the performance of different schemes and their advantages and disadvantages were comprehensively compared and analyzed, and finally future research trends were summarized and looked forward to.…”
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The secondary use of the Finnish national health data repository Kanta – opportunities and obstacles by Emmi Eronen, Alpo Olavi Värri, Miika Järvinen

“…While protecting the privacy of the individuals’ data in the Kanta Services, improvements to the secondary use law are suggested. …”
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