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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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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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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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

“…To begin, we offer an IoT data encryption strategy in which edge devices can send data to a nearby cloud network for data processing while maintaining privacy. …”
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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 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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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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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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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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MAD-RAPPEL: Mobility Aware Data Replacement And Prefetching Policy Enrooted LBS by Ajay K. Gupta, Udai Shanker

“…The features of mobile devices are being continuously upgraded to provide quality of services to the mobile user seeking location-based information by allowing the usage of context-aware data. To protect an individual’s location & his information to untrusted entity, a multi-level caching, i.e., Mobility Aware Data Replacement & Prefetching Policy Enrooted LBS using spatial k-anonymity (MAD-RAPPEL) is being proposed in this paper. …”
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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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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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Privacy and security challenges of the digital twin: systematic literature review by Marija Kuštelega, Renata Mekovec, Ahmed Shareef

“…The results indicate that the privacy and security challenges for digital twin implementation are complicated and may be divided into six primary groups: (1) data privacy, (2) data security, (3) data management, (4) data infrastructure and standardization, (5) ethical and moral issues, (6) legal and social issues. …”
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Exposing privacy risks in indoor air pollution monitoring systems by Singh Krishna, Gujar Shreyash, Chaudhari Sachin, Kumaraguru Ponnurangam

“…Less detailed data like hourly averages, can be used to make meaningful conclusions that might intrude on an individual’s privacy. …”
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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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Differential privacy budget optimization based on deep learning in IoT by Dan LUO, Ruzhi XU, Zhitao GUAN

“…In order to effectively process the massive data brought by the large-scale application of the internet of things (IoT), deep learning is widely used in IoT environment.However, in the training process of deep learning, there are security threats such as reasoning attacks and model reverse attacks, which can lead to the leakage of the original data input to the model.Applying differential privacy to protect the training process parameters of the deep model is an effective way to solve this problem.A differential privacy budget optimization method was proposed based on deep learning in IoT, which adaptively allocates different budgets according to the iterative change of parameters.In order to avoid the excessive noise, a regularization term was introduced to constrain the disturbance term.Preventing the neural network from over fitting also helps to learn the salient features of the model.Experiments show that this method can effectively enhance the generalization ability of the model.As the number of iterations increases, the accuracy of the model trained after adding noise is almost the same as that obtained by training using the original data, which not only achieves privacy protection, but also guarantees the availability, which means balance the privacy and availability.…”
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