Privacy under threat – The intersection of IoT and mass surveillance by Siniša Domazet, Darko Marković, Tatjana Skakavac

“…It has been shown that there are issues with applying existing regulations to IoT and mass surveillance and that no universal legal framework currently exists to protect the right to privacy. The use of IoT technology, especially given the rapid development of artificial intelligence, will in the future raise numerous dilemmas regarding the entities responsible for collecting personal data, the consents required for data usage and processing, where the collected personal data will be used, and for what purposes. …”
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Exploring the Use of AI in Qualitative Data Analysis: Comparing Manual Processing with Avidnote for Theme Generation by S. M. Akramul Kabir, Fareeha Ali, Rana Lotfy Ahmed, Ruqayya Sulaiman-Hill

“…However, fundamental concerns persist regarding the robustness, generalisability, credibility, reliability and trustworthiness of qualitative research when using AI technologies. Ethical considerations, such as data security and privacy, also need to be addressed in research settings. …”
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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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Ensuring Privacy and Security on Banking Websites in Malaysia: A Cookies Scanner Solution by Yi Hong Tay, Shih Yin Ooi, Ying Han Pang, Ying Huey Gan, Sook Ling Lew

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A Differential Privacy Framework with Adjustable Efficiency–Utility Trade-Offs for Data Collection by Jongwook Kim, Sae-Hong Cho

“…The widespread use of mobile devices has led to the continuous collection of vast amounts of user-generated data, supporting data-driven decisions across a variety of fields. …”
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Time Series Classification Using Federated Convolutional Neural Networks and Image-Based Representations by Felipe A. R. Silva, Omid Orang, Fabricio Javier Erazo-Costa, Petronio C. L. Silva, Pedro H. Barros, Ricardo P. M. Ferreira, Frederico Gadelha Guimaraes

“…Federated Learning (FL) enables collaborative training across distributed clients while ensuring data privacy. The method’s effectiveness is evaluated using 40 datasets from the UCR Archive. …”
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Federated target trial emulation using distributed observational data for treatment effect estimation by Haoyang Li, Chengxi Zang, Zhenxing Xu, Weishen Pan, Suraj Rajendran, Yong Chen, Fei Wang

“…Abstract Target trial emulation (TTE) aims to estimate treatment effects by simulating randomized controlled trials using real-world observational data. Applying TTE across distributed datasets shows great promise in improving generalizability and power but is always infeasible due to privacy and data-sharing constraints. …”
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Generating unseen diseases patient data using ontology enhanced generative adversarial networks by Chang Sun, Michel Dumontier

“…Abstract Generating realistic synthetic health data (e.g., electronic health records), holds promise for fundamental research, AI model development, and enhancing data privacy safeguards. …”
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Predicting financial default risks: A machine learning approach using smartphone data by Shinta Palupi, Gunawan, Ririn Kusdyawati, Richki Hardi, Rana Zabrina

“…This study leverages machine learning (ML) techniques to predict financial default risks using smartphone data, providing a novel approach to financial risk assessment. …”
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Sample selection using multi-task autoencoders in federated learning with non-IID data by Emre Ardıç, Yakup Genç

“…Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. …”
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The Dual Nature of Trust in Participatory Sciences: An Investigation into Data Quality and Household Privacy Preferences by Danielle Lin Hunter, Valerie Johnson, Caren Cooper

“…In Crowd the Tap, we engaged participants through facilitator organizations including high schools, faith communities, universities, and a corporate volunteer program. We used Kruskal Wallis tests and chi-square tests with Bonferroni post hoc tests to assess how data quality and privacy preferences differed across facilitator groups and amongst those who participated in the project independently (unfacilitated). …”
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Social media in health care- - Ethically challenging - -Dangerous but attractive- -use with caution by Shrikant B. Mali

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Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning by Milad Rahmati

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A Comprehensive Study of Traditional and Deep-learning Schemes for Privacy and Data Security in the Cloud by mohammed sheet, Melad saeed

“…However, it faces great difficulties in ensuring data confidentiality and privacy. People hesitate to use it due to the risk of innumerable attacks and security breaches. …”
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Efficient and privacy-preserving certificateless data aggregation in Internet of things–enabled smart grid by Aijing Sun, Axin Wu, Xiaokun Zheng, Fangyuan Ren

“…If the user’s electricity consumption is transmitted in plaintext, the data may be used by some illegal users. At the same time, malicious users may send false data such that the control center makes a wrong power resource scheduling. …”
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Soulbound Tokens: Enabler for Privacy-Aware and Decentralized Authentication Mechanism in Medical Data Storage by Biagio Boi, Franco Cirillo, Marco De Santis, Christian Esposito

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Efficient privacy-preserving image retrieval scheme over outsourced data with multi-user by Xiangyu WANG, Jianfeng MA, Yinbin MIAO

“…The traditional privacy-preserving image retrieval schemes not only bring large computational and communication overhead,but also cannot protect the image and query privacy in multi-user scenarios.To solve above problems,an efficient privacy-preserving content-based image retrieval scheme was proposed in multi-user scenarios.The scheme used Euclidean distance comparison technique to rank the pictures according to similarity of picture feature vectors and return top-k returned.Meanwhile,the efficient key conversion protocol designed in proposed image retrieval scheme allowed each search user to generate queries based on his own private key so that he can retrieval encrypted images generated by different data owners.Strict security analysis shows that the user privacy and cloud data security can be well protected during the image retrieval process,and the performance analysis using real-world dataset shows that the proposed image retrieval scheme is efficient and feasible in practical applications.…”
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Shuffled differential privacy protection method for K-Modes clustering data collection and publication by Weijin JIANG, Yilin CHEN, Yuqing HAN, Yuting WU, Wei ZHOU, Haijuan WANG

“…Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms.…”
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A controllable privacy data transmission mechanism for Internet of things system based on blockchain by ZiXiang Nie, YuanZhenTai Long, SenLin Zhang, YueMing Lu

“…With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. …”
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Preserving Big Data Privacy in Cloud Environments Based on Homomorphic Encryption and Distributed Clustering by Shatha A. Baker

“… Cloud computing has grown in popularity in recent years because to its efficiency, flexibility, scalability, and the services it provides for data storage and processing. Still, big businesses and organizations have severe concerns about protecting privacy and data security while processing these massive volumes of data. …”
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