A Combined Approach of Heat Map Confusion and Local Differential Privacy for the Anonymization of Mobility Data by Christian Dürr, Gabriele S. Gühring

“…Mobility data plays a crucial role in modern location-based services (LBSs), yet it poses significant privacy risks, as it can reveal highly sensitive information such as home locations and behavioral patterns. …”
Get full text

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. …”
Get full text

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.…”
Get full text

Sinergi Pemanfaatan Big Data dengan Sharing Data Privacy Pengguna: Efek Menawarkan Kompensasi dalam Meyakinkan Pengguna E-Wallet by Christina Sudyasjayanti, Laili Rahmania

Get full text

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. …”
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

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

Get full text

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. …”
Get full text

(<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. …”
Get full text

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”. …”
Get full text

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.…”
Get full text

Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity by Shubhi Shukla, Suraksha Rajkumar, Aditi Sinha, Mohamed Esha, Konguvel Elango, Vidhya Sampath

“…This mitigates adversarial attacks and prevents data leakage. The proposed work uses the Breast Cancer Wisconsin Diagnostic dataset to address critical challenges such as data heterogeneity, privacy-accuracy trade-offs, and computational overhead. …”
Get full text

Privacy-Preserving Poisoning-Resistant Blockchain-Based Federated Learning for Data Sharing in the Internet of Medical Things by Xudong Zhu, Hui Li

“…Although current blockchain-based federated learning (BFL) approaches aim to resolve these issues, two persistent security weaknesses remain: privacy leakage and poisoning attacks. This study proposes a privacy-preserving poisoning-resistant blockchain-based federated learning (PPBFL) scheme for secure IoMT data sharing. …”
Get full text

Optimizing data privacy and security measures for critical infrastructures via IoT based ADP2S technique by Zhenyu Xu, Jinming Wang, Shujuan Feng, Salwa Othmen, Chahira Lhioui, Aymen Flah, Zdenek Slanina

“…This paper uses a reptile search optimization algorithm to offer attuned data protection with privacy scheme (ADP2S). …”
Get full text

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. …”
Get full text

Privacy-Preserving Data Sharing and Computing for Outsourced Policy Iteration with Attempt Records from Multiple Users by Bangyan Chen, Jun Ye

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text