De novo non-canonical nanopore basecalling enables private communication using heavily-modified DNA data at single-molecule level by Qingyuan Fan, Xuyang Zhao, Junyao Li, Ronghui Liu, Ming Liu, Qishun Feng, Yanping Long, Yang Fu, Jixian Zhai, Qing Pan, Yi Li

“…Abstract Hidden messages in DNA molecules by employing chemical modifications has been suggested for private data storage and transmission at high information density. …”
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Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data by Jennifer K Quint, Aziz Sheikh, Chris Robertson, Srinivasa Vittal Katikireddi, Emily Moore, Colin R Simpson, Luke Daines, Eleftheria Vasileiou, Syed Ahmar Shah, Rachel H Mulholland, Vicky Hammersley, Steven Kerr, Ting Shi, David Weatherill, Elisa Pesenti

“…In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID.Methods and analysis We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. …”
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Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model by Shanshan Wang, Jingwu Liu

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New Images of the Globalized World Crossed by Artificial Intelligence by Vanina Rodríguez

“…Data privacy and security are also major challenges in the use of AI to generate and select content. …”
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Data protection impact assessment system in the mode of risk management by Ying CHENG

“…In the era of big data,the risk management approach has been broadly applied in the field of personal information protection.Data protection impact assessment has become an important system to promote data protection.It takes the provisions of the data protection impact assessment of the European General Data Protection Regulation (GDPR) 2016 as the sample.By using the literature research and empirical analysis method,it analyzes in depth the theoretical background,rise and evolution,meaning and scope of data protection impact assessment to establish a standardized and specific impact assessment system as well as promote personal information protection.Assessment content includes not only privacy risk assessment,but also data security,data quality and non-discrimination.Data protection impact assessment should be set as a mandatory obligation for data processing activities that are likely to result in high risks.The evaluation process shall take the advices from stakeholders to reflect their benefits.The external supervision should be strengthened and the assessment report shall be published properly.…”
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Critical Factors in Young People’s Use and Non-Use of AI Technology for Emotion Regulation: A Pilot Study by Junyu Wang, Hongying Tang, Siu-Shing Man, Yingwei Chen, Shuzhang Zhou, Hoi-Shou (Alan) Chan

“…Data were collected through semi-structured face-to-face interviews and were analysed using NVivo 11 software. …”
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Novel Bounds for Incremental Hessian Estimation With Application to Zeroth-Order Federated Learning by Alessio Maritan, Luca Schenato, Subhrakanti Dey

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Blockchain-Enabled Federated Learning in Healthcare: Survey and State-of-the-Art by Nasim Nezhadsistani, Naghmeh S. Moayedian, Burkhard Stiller

“…Centralization of health data to train ML models does pose privacy, ownership, and regulatory problems. …”
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Multi-authority attribute hidden for electronic medical record sharing scheme based on blockchain by Lin JIN, Youliang TIAN

“…Currently, there is no data exchanging and sharing between different hospitals, and it is easy to form data islands.At the same time, regional medical data contains a large amount of sensitive information of patients.The public acquisition, sharing and circulation of these data will lead to malicious tampering, theft, abuse and loss of ownership, thereby revealing patient privacy.In addition, the size of medical data is enormous and the data is unstructured, then it is more difficult to prevent and hold accountable some highly targeted malicious attacks, such as malicious attacks on medical data theft, tampering, and extortion.In view of the above problems, a blockchain-based on multi-authority attribute hidden electronic medical record sharing scheme was proposed to achieve fine-grained access to shared electronic medical records while ensuring patient privacy.The Multi-Authorization Attribute Encryption (MA-ABE) algorithm was introduced, which used multi-authority organizations to manage decentralized attributes.It also used hash functions to identify different users, in order to effectively resist collusion attacks between users with different authorizations.Besides, the linear secrets sharing scheme (LSSS) was used to realize partial hiding of attributes, and the attributes were divided into two parts:attribute name and attribute value.In addition, combined with the characteristics of blockchain openness, transparency and tamper-proof, the design of access policy can update the algorithm.Based on the access policy update algorithm, the policy block was added.The new access policy was uploaded to the blockchain to form a policy update traceability chain, which can realize distributed and reliable access control management under the condition of hidden policy.It can also support data privacy protection at the same time, and traceability of user behavior.The theoretical proof and experimental analysis have proved that this scheme protect attribute privacy effectively, while reduces computational overhead.…”
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What if Facebook goes down? Ethical and legal considerations for the demise of big tech by Carl Öhman, Nikita Aggarwal

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Federated Learning for Human Activity Recognition: Overview, Advances, and Challenges by Ons Aouedi, Alessio Sacco, Latif U. Khan, Dinh C. Nguyen, Mohsen Guizani

“…Federated Learning (FL) is a promising paradigm for HAR that enables the collaborative training of machine learning models on decentralized devices while preserving data privacy. It improves not only data privacy but also training efficiency as it utilizes the computing power and data of potentially millions of smart devices for parallel training. …”
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PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation by Shengyuan Liu, Ruofan Zhang, Mengjie Fang, Hailin Li, Tianwang Xun, Zipei Wang, Wenting Shang, Jie Tian, Di Dong

“…Abstract Federated learning (FL) has shown great potential in addressing data privacy issues in medical image analysis. However, varying data distributions across different sites can create challenges in aggregating client models and achieving good global model performance. …”
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Blockchain-Assisted Hierarchical Attribute-Based Encryption Scheme for Secure Information Sharing in Industrial Internet of Things by A. Sasikumar, Logesh Ravi, Malathi Devarajan, A. Selvalakshmi, Abdulaziz Turki Almaktoom, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed

“…In addition, we developed a blockchain-integrated data-sharing scheme that makes it possible for users to share data via the use of edge and cloud storage. …”
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Privacy Harm and Non-Compliance from a Legal Perspective by Suvineetha Herath, Haywood Gelman, Lisa McKee

“…Increased data mining techniques used to analyze big data have posed significant risks to data security and privacy. …”
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Organizations` Responsibility in Maintaining the Security of Personal Data posted Online by Romanian Consumers: an Exploratory Analysis of Facebook and Linkedin by Andreea Ionescu, Laurenţiu-Dan Anghel, Gheorghe Jinga

“…The information was gathered with the help of an online questionnaire, administered to people over 18 years old. It is a very useful and needed tool for Romanian companies, as it presents the users’ point of view, allowing them to find the best and most ethical way to do social data mining or use consumers’ private information, disclosed on such sites. …”
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Federated edge learning model based on multi-level proxy permissioned blockchain by GE Li’na, LI Haiao, WANG Jie

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Federated edge learning model based on multi-level proxy permissioned blockchain by GE Li’na, LI Haiao, WANG Jie

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Cryptographic Techniques in Artificial Intelligence Security: A Bibliometric Review by Hamed Taherdoost, Tuan-Vinh Le, Khadija Slimani

“…Traditional AI systems often lack robust security measures, making them vulnerable to adversarial attacks, data breaches, and privacy violations. Cryptography has emerged as a crucial component in enhancing AI security by ensuring data confidentiality, authentication, and integrity. …”
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SafeTrack: Secure Tracking Protocol for Mobile Sensor Nodes in Unstable Wireless Sensor Networks by Ali Adnan Al-Khazraji, Fatimah Abdulridha Rashid

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Detecting Advanced Persistent Threat Exfiltration With Ensemble Deep Learning Tree Models and Novel Detection Metrics by Xiaojuan Cai, Haibo Zhang, Chuadhry Mujeeb Ahmed, Hiroshi Koide

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