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

Get full text

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

Get full text

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. …”
Get full text

SafeTrack: Secure Tracking Protocol for Mobile Sensor Nodes in Unstable Wireless Sensor Networks by Ali Adnan Al-Khazraji, Fatimah Abdulridha Rashid

Get full text

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

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Artificial Intelligence and Privacy: The Urgent Need for Children’s Media Literacy by Katharine Sarikakis, Angeliki Chatziefraimidou

“… Protecting children’s privacy continues to challenge policymakers and citizens alike in the media age and debates often point to the need for data protection literacy. …”
Get full text

Decentralized Blockchain-Based Authentication and Interplanetary File System-Based Data Management Protocol for Internet of Things Using Ascon by Hiba Belfqih, Abderrahim Abdellaoui

Get full text

Fine-grained privacy operation control method for layout documents by Peijie YIN, Fenghua LI, Ben NIU, Haiyang LUO, Bin KUANG, Lingcui ZHANG

“…In view of the problem of privacy information disclosure caused by uncontrolled forwarding in the context of frequent exchange of privacy information, a fine-grained privacy operation control method for layout documents was proposed, which could achieve differentiated fine-grained privacy operation control according to the requirements of the sharer during the sharing process of privacy information.For the received multimodal layout document, the existing privacy operation control strategy was extracted, which combined the current sharer’s use attribute and the receiver’s privacy protection ability and other factors.The privacy operation control strategy was generated iteratively, and an abstract control strategy generation algorithm framework was given.Based on the iterative privacy operation control strategy and combined with specific operation scenarios, the differentiated data-masking control, exchange boundary control and local use control were carried out for different modes of information components, and the abstract privacy operation control algorithm framework was given.A prototype system for privacy operation control of OFD (open fixed-layout document) was developed to verify the above algorithms.The generation and delivery of iterative privacy operation control strategy based on friendship, as well as the differential data-masking control, exchange boundary control and local use control of OFD were implemented in the instant messaging system.…”
Get full text

Social media in health care- - Ethically challenging - -Dangerous but attractive- -use with caution by Shrikant B. Mali

Get full text

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. …”
Get full text

A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…Among the existing botnet detection methods, whether they extract deterministic traffic interaction features, use DNS traffic, or methods based on raw traffic bytes, these methods focus on the detection performance of the detection model and ignore possible privacy leaks. …”
Get full text

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. …”
Get full text

An efficient secure predictive demand forecasting system using Ethereum virtual machine by Himani Saraswat, Mahesh Manchanda, Sanjay Jasola

“…The system leverages the Ethereum virtual machine to establish a secure, decentralized, and tamper‐resistant platform for demand prediction while ensuring data integrity and privacy. By utilizing the capabilities of smart contracts and decentralized applications within the Ethereum ecosystem, the proposed system offers an efficient and transparent solution for demand forecasting challenges. …”
Get full text

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.…”
Get full text

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.…”
Get full text

Voice Fence Wall: User-optional voice privacy transmission by Li Luo, Yining Liu

“…Sensors are widely applied in the collection of voice data. Since many attributes of voice data are sensitive such as user emotions, identity, raw voice collection may lead serious privacy threat. …”
Get full text

Policy-Based Smart Contracts Management for IoT Privacy Preservation by Mohsen Rouached, Aymen Akremi, Mouna Macherki, Naoufel Kraiem

“…This paper addresses the challenge of preserving user privacy within the Internet of Things (IoT) ecosystem using blockchain technology. …”
Get full text