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

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

“…And most methods are combined with machine learning and deep learning technologies, which require a large amount of training data to obtain high-precision detection models. Therefore, preventing malicious persons from stealing data to infer privacy during the botnet detection process has become an issue worth pondering. …”
Get full text

Sentimental analysis based federated learning privacy detection in fake web recommendations using blockchain model by Jitendra Kumar Samriya, Amit Kumar, Ashok Bhansali, Meena Malik, Varsha Arya, Wadee Alhalabi, Bassma Saleh Alsulami, Brij B. Gupta

“…This work offers an experimental analysis of diverse sentiment data-driven fake recommendation datasets, evaluating performance using accuracy, precision, recall, and F-measure metrics. …”
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

BPS-FL: Blockchain-Based Privacy-Preserving and Secure Federated Learning by Jianping Yu, Hang Yao, Kai Ouyang, Xiaojun Cao, Lianming Zhang

“…To resist malicious gradient attacks, we design a Byzantine-robust aggregation protocol for BPS-FL to realize the cipher-text level secure model aggregation. Moreover, we use a blockchain as the underlying distributed architecture to record all learning processes, which ensures the immutability and traceability of the data. …”
Get full text

Privacy as Invisibility: Pervasive Surveillance and the Privatization of Peer-to-Peer Systems by Francesca Musiani

“…Yet, it also suggests that the richness of today’s landscape of P2P technology development and use, mainly in the field of Internet-based services, opens up new dimensions to the conceptualization of privacy, and may give room to a more articulate definition of the concept as related to P2P technology; one that includes not only the need of protection from external attacks, and the temporary outcomes of the competition between surveillance and counter-surveillance measures, but also issues such as user empowerment through better control over personal information, reconfiguration of data management practices, and removal of intermediaries in sharing and communication activities. …”
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

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

Comprehensive Review on Facets of Cloud Computing in Context of Security and Privacy by Saurabh Aggarwal, Ashish Khanna, Abhilash Maroju

“…Cloud adoption is hampered by the serious security and privacy issues that arise when data and apps are outsourced to unaffiliated cloud providers. …”
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

SpyKing—Privacy-preserving framework for Spiking Neural Networks by Farzad Nikfam, Alberto Marchisio, Maurizio Martina, Muhammad Shafique

“…However, the vast amount of data they process is not always secure, posing potential risks to privacy and safety. …”
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

Scalable Distributed Reproduction Numbers of Network Epidemics With Differential Privacy by Bo Chen, Baike She, Calvin Hawkins, Philip E. Pare, Matthew T. Hale

“…Reproduction numbers are widely used to analyze epidemic spreading processes over networks. …”
Get full text

Towards edge-collaborative, lightweight and privacy-preserving classification framework by Jinbo XIONG, Yongjie ZHOU, Renwan BI, Liang WAN, Youliang TIAN

“…Aiming at the problems of data leakage of perceptual image and computational inefficiency of privacy-preserving classification framework in edge-side computing environment, a lightweight and privacy-preserving classification framework (PPCF) was proposed to supports encryption feature extraction and classification, and achieve the goal of data transmission and computing security under the collaborative classification process of edge nodes.Firstly, a series of secure computing protocols were designed based on additive secret sharing.Furthermore, two non-collusive edge servers were used to perform secure convolution, secure batch normalization, secure activation, secure pooling and other deep neural network computing layers to realize PPCF.Theoretical and security analysis indicate that PPCF has excellent accuracy and proved to be security.Actual performance evaluation show that PPCF can achieve the same classification accuracy as plaintext environment.At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPCF has obvious advantages in terms of computational cost and communication overhead.…”
Get full text

Privacy-preserving method for face recognition based on homomorphic encryption. by Zhigang Song, Gong Wang, Wenqin Yang, Yunliang Li, Yinsheng Yu, Zeli Wang, Xianghan Zheng, Yang Yang

“…Performance analysis indicates that the HE_FaceNet framework successfully protects facial data privacy while maintaining high recognition accuracy, and the optimization scheme demonstrates high accuracy and significant computational efficiency across facial datasets of varying sizes.…”
Get full text

Balancing Security and Privacy: Web Bot Detection, Privacy Challenges, and Regulatory Compliance under the GDPR and AI Act [version 1; peer review: 2 approved] by Javier Martínez Llamas, Davy Preuveneers, Koen Vranckaert, Wouter Joosen

“…Additionally, the study dives into the use of Privacy Enhancing Technologies (PETs) to strike a balance between bot detection and user privacy. …”
Get full text

GDPR-oriented intelligent checking method of privacy policies compliance by Xin LI, Peng TANG, Xiheng ZHANG, Weidong QIU, Hong HUI

“…The implementation of the EU’s General Data Protection Regulation (GDPR) has resulted in the imposition of over 300 fines since its inception in 2018.These fines include significant penalties for prominent companies like Google, which were penalized for their failure to provide transparent and comprehensible privacy policies.The GDPR, known as the strictest data protection laws in history, has made companies worldwide more cautious when offering cross-border services, particularly to the European Union.The regulation's territorial scope stipulates that it applies to any company providing services to EU citizens, irrespective of their location.This implies that companies worldwide, including domestic enterprises, are required to ensure compliance with GDPR in their privacy policies, especially those involved in international operations.To meet this requirement, an intelligent detection method was introduced.Machine learning and automation technologies were utilized to automatically extract privacy policies from online service companies.The policies were converted into a standardized format with a hierarchical structure.Through natural language processing, the privacy policies were classified, allowing for the identification of relevant GDPR concepts.In addition, a constructed GDPR taxonomy was used in the detection mechanism to identify any missing concepts as required by GDPR.This approach facilitated intelligent detection of GDPR-oriented privacy policy compliance, providing support to domestic enterprises while they provided cross-border services to EU users.Analysis of the corpus samples reveals the current situation that mainstream online service companies generally fail to meet GDPR compliance requirements.…”
Get full text

Privacy-Preserving Image Captioning with Partial Encryption and Deep Learning by Antoinette Deborah Martin, Inkyu Moon

“…Although image captioning has gained remarkable interest, privacy concerns are raised because it relies heavily on images, and there is a risk of exposing sensitive information in the image data. …”
Get full text