Practical and privacy-preserving geo-social-based POI recommendation by Qi Xu, Hui Zhu, Yandong Zheng, Fengwei Wang, Le Gao

“…Specifically, we first utilize the quad tree to organize geographic data and the MinHash method to index social data. …”
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Privacy-preserving federated learning framework with dynamic weight aggregation by Zuobin YING, Yichen FANG, Yiwen ZHANG

“…There are two problems with the privacy-preserving federal learning framework under an unreliable central server.① A fixed weight, typically the size of each participant’s dataset, is used when aggregating distributed learning models on the central server.However, different participants have non-independent and homogeneously distributed data, then setting fixed aggregation weights would prevent the global model from achieving optimal utility.② Existing frameworks are built on the assumption that the central server is honest, and do not consider the problem of data privacy leakage of participants due to the untrustworthiness of the central server.To address the above issues, based on the popular DP-FedAvg algorithm, a privacy-preserving federated learning DP-DFL algorithm for dynamic weight aggregation under a non-trusted central server was proposed which set a dynamic model aggregation weight.The proposed algorithm learned the model aggregation weight in federated learning directly from the data of different participants, and thus it is applicable to non-independent homogeneously distributed data environment.In addition, the privacy of model parameters was protected using noise in the local model privacy protection phase, which satisfied the untrustworthy central server setting and thus reduced the risk of privacy leakage in the upload of model parameters from local participants.Experiments on dataset CIFAR-10 demonstrate that the DP-DFL algorithm not only provides local privacy guarantees, but also achieves higher accuracy rates with an average accuracy improvement of 2.09% compared to the DP-FedAvg algorithm models.…”
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Sinergi Pemanfaatan Big Data dengan Sharing Data Privacy Pengguna: Efek Menawarkan Kompensasi dalam Meyakinkan Pengguna E-Wallet by Christina Sudyasjayanti, Laili Rahmania

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THE USE OF HUMAN CHIP IMPLANTS FROM THE HADITH PERSPECTIVE by Vira Fharadillah, Muhammad Ghifari, Abil Ash

“…Chip implants are permitted for clear medical purposes that do not cause harm, while their use for non-medical purposes or those that potentially violate privacy and pose risks is prohibited in Islam. …”
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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. …”
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Efficient Access Control for Video Anomaly Detection Using ABE-Based User-Level Revocation with Ciphertext and Index Updates by Lu Jiang, Jielu Yan, Weizhi Xian, Xuekai Wei, Xiaofeng Liao

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(<italic>r, k, &#x03B5;</italic>)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, <italic>k</italic>-Anonymity, and <itali... by Burak Cem Kara, Can Eyupoglu, Oktay Karakus

“…The general data protection regulation (GDPR) implementation, on the other hand, has introduced extensive control over the use of individuals&#x2019; personal information and placed many limits. …”
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Security Data Aggregation with Recoverable Data in Heterogeneous Wireless Sensor Network by Lusheng Shi, Huibo Zhu, Lin Chen

“…The algorithm uses homomorphism encryption techniques based on elliptic curve to address data privacy protection, and uses an efficient aggregate signature scheme to ensure data integrity and authenticity. …”
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Navigating the EU data governance labyrinth: A business perspective on data sharing in the financial sector by Eugénie Coche, Ans Kolk, Martijn Dekker

“…With policy-making (“on the books”) centred on guaranteeing data privacy and data security whilst promoting innovation, firms face complexities when implementing this framework “on the ground”. …”
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Continuous location privacy protection mechanism based on differential privacy by Hongtao LI, Xiaoyu REN, Jie WANG, Jianfeng MA

“…Aiming at the problem of users’ location privacy leakage caused by continuously using LBS, a road privacy level (RPL) algorithm was proposed based on road topological network, which divided the privacy level of the road sections around the sensitive locations.Then, a differential privacy location protection mechanism (DPLPM) was proposed.Privacy budget was allocated for sensitive road sections and Laplace noise was added to realize the privacy protection of location data.The experimental results show that the mechanism has high data availability while protecting the privacy of location information.…”
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Exploration of Reproductive Health Apps’ Data Privacy Policies and the Risks Posed to Users: Qualitative Content Analysis by Nina Zadushlivy, Rizwana Biviji, Karmen S Williams

“…A qualitative content analysis of the apps and a review of the literature on data use policies, governmental data privacy regulations, and best practices for mobile app data privacy were conducted between January 2023 and July 2023. …”
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Privacy-Preserving Data Sharing and Computing for Outsourced Policy Iteration with Attempt Records from Multiple Users by Bangyan Chen, Jun Ye

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Navigating Data Privacy in Digital Public Services: Public Perceptions and Policy Implications. Romania Case Study by Mircea POPA

“…However, this reliance on data has raised critical concerns about privacy, security, and ethical data use. …”
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Enabling trustworthy personal data protection in eHealth and well-being services through privacy-by-design by Tomás Robles, Borja Bordel, Ramón Alcarria, Diego Sánchez-de-Rivera

“…Nevertheless, the adequate implementation of these rights is not guaranteed, as services use the received data with commercial purposes. …”
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A Data Protection Method for the Electricity Business Environment Based on Differential Privacy and Federal Incentive Mechanisms by Xu Zhou, Hongshan Luo, Simin Chen, Yuling He

“…This paper conducts experiments using the data of Shenzhen City, Guangdong Province. …”
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Worldwide willingness to share health data high but privacy, consent and transparency paramount, a meta-analysis by Quita Olsen, Amalie Dyda, Leanna Woods, Elton Lobo, Rebekah Eden, Michelle A. Krahe, Bernadette Richards, Nalini Pather, Lesley McGee, Clair Sullivan, Jason D. Pole

“…Articles were included if they quantitatively examined the primary outcome; the public’s willingness to share health data for secondary use, while secondary outcomes included demographic and perception measures associated with willingness to share. …”
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Membership Inference Attacks Fueled by Few-Shot Learning to Detect Privacy Leakage and Address Data Integrity by Daniel Jiménez-López, Nuria Rodríguez-Barroso, M. Victoria Luzón, Javier Del Ser, Francisco Herrera

“…Deep learning models have an intrinsic privacy issue as they memorize parts of their training data, creating a privacy leakage. …”
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Federated Analysis With Differential Privacy in Oncology Research: Longitudinal Observational Study Across Hospital Data Warehouses by Théo Ryffel, Perrine Créquit, Maëlle Baillet, Jason Paumier, Yasmine Marfoq, Olivier Girardot, Thierry Chanet, Ronan Sy, Louise Bayssat, Julien Mazières, Vincent Vuiblet, Julien Ancel, Maxime Dewolf, François Margraff, Camille Bachot, Jacek Chmiel

“…Despite some pioneering work, federated analytics is still not widely used on real-world data, and to our knowledge, no real-world study has yet combined it with other privacy-enhancing techniques such as differential privacy (DP). …”
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FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence by Rahul Haripriya, Nilay Khare, Manish Pandey, Shrijal Patel, Jaytrilok Choudhary, Dhirendra Pratap Singh, Surendra Solanki, Duansh Sharma

“…As machine learning (ML) and artificial intelligence (AI) increasingly influence such decisions, promoting responsible AI that minimizes bias while preserving data privacy has become essential. However, existing fairness-aware models are often centralized or ill-equipped to handle non-IID data, limiting their real-world applicability. …”
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Preliminary study on the construction of a data privacy protection course based on a teaching-in-practice range by Zhe SUN, Hong NING, Lihua YIN, Binxing FANG

“…Since China’s Data Security Law, Personal Information Protection Law and related laws were formalized, demand for privacy protection technology talents has increased sharply, and data privacy protection courses have been gradually offered in the cyberspace security majors of various universities.Building on longstanding practices in data security research and teaching, the teaching team of “Academician Fang Binxing’s Experimental Class” (referred to as “Fang Class”) at Guangzhou University has proposed a teaching method for data privacy protection based on a teaching-in-practice range.In the selection of teaching course content, the teaching team selected eight typical key privacy protection techniques including anonymity model, differential privacy, searchable encryption, ciphertext computation, adversarial training, multimedia privacy protection, privacy policy conflict resolution, and privacy violation traceability.Besides, the corresponding teaching modules were designed, which were deployed in the teaching practice range for students to learn and train.Three teaching methods were designed, including the knowledge and application oriented teaching method which integrates theory and programming, the engineering practice oriented teaching method based on algorithm extension and adaptation, and the comprehensive practice oriented teaching method for practical application scenarios.Then the closed loop of “learning-doing-using” knowledge learning and application was realized.Through three years of privacy protection teaching practice, the “Fang class” has achieved remarkable results in cultivating students’ knowledge application ability, engineering practice ability and comprehensive innovation ability, which provided useful discussion for the construction of the initial course of data privacy protection.…”
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