Differential Privacy and Collective Bargaining over Workplace Data by Sandy J.J. Gould

“…I argue that using differential privacy, a technique for processing data that makes it harder to determine who contributed data to a dataset, would remove an obstacle to employers sharing workplace data with worker representatives.…”
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Research on data integration privacy preservation mechanism for DaaS by Zhi-gang ZHOU, Hong-li ZHANG, Xiang-zhan YU, Pan-pan LI

“…In addition, the corres ding fake data set was used to assure the balanced distribution of data in each part, which realized privacy protection of data integration. …”
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Aggregated privacy-preserving auditing for cloud data integrity by Kai HE, Chuan-he HUANG, Xiao-mao WANG, Jing WANG, Jiao-li SHI

“…To solve the problem of data integrity in cloud storage,an aggregated privacy-preserving auditing scheme was proposed.To preserve data privacy against the auditor,data proof and tag proof were encrypted and combined by using the bilinearity property of the bilinear pairing on the cloud server.Furthermore,an efficient index mechanism was designed to support dynamic auditing,which could ensure that data update operations did not lead to high additional computation or communication cost.Meanwhile,an aggregation method for different proofs was designed to handle multiple auditing requests.Thus the proposed scheme could also support batch auditing for multiple owners and multiple clouds and multiple files.The communication cost of batch auditing was independent of the number of auditing requests.The theoretical analysis and experimental results show that the proposed scheme is provably secure.Compared with existing auditing scheme,the efficacy of the proposed individual auditing and batch auditing improves 21.5% and 31.8% respectively.…”
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Survey of artificial intelligence data security and privacy protection by Kui REN, Quanrun MENG, Shoukun YAN, Zhan QIN

“…Artificial intelligence and deep learning algorithms are developing rapidly.These emerging techniques have been widely used in audio and video recognition, natural language processing and other fields.However, in recent years, researchers have found that there are many security risks in the current mainstream artificial intelligence model, and these problems will limit the development of AI.Therefore, the data security and privacy protection was studied in AI.For data and privacy leakage, the model output based and model update based problem of data leakage were studied.In the model output based problem of data leakage, the principles and research status of model extraction attack, model inversion attack and membership inference attack were discussed.In the model update based problem of data leakage, how attackers steal private data in the process of distributed training was discussed.For data and privacy protection, three kinds of defense methods, namely model structure defense, information confusion defense and query control defense were studied.In summarize, the theoretical foundations, classic algorithms of data inference attack techniques were introduced.A few research efforts on the defense techniques were described in order to provoke further research efforts in this critical area.…”
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A Review of Differential Privacy in Individual Data Release by Jun Wang, Shubo Liu, Yongkai Li

“…The rapid development of mobile technology has improved users' quality of treatment, and tremendous amounts of medical information are readily available and widely used in data analysis and application, which bring on serious threats to users' privacy. …”
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Technology Policy Recommendations Using Artificial Intelligence by Ashita Anuga, Minh Nguyen, Dominick Perini, Andrei Svetovidov, Amanda Tolman, Qasim Wani, Feras A. Batarseh

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Enhancing privacy and traceability of public health insurance claim system using blockchain technology by Andry Alamsyah, I. Putu Sadhu Setiawan

“…However, this extensive coverage raises substantial concerns about data privacy and traceability, particularly during the claim process, as policyholders currently have limited control over and insight into how their data is accessed and used.MethodsTo address these challenges, we propose a blockchain-based model designed to enhance policyholders’ private control over data access and improve traceability throughout the NHI claim process. …”
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A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation by Rahul Haripriya, Nilay Khare, Manish Pandey, Sreemoyee Biswas

“…Abstract The exponential growth of Big Data in healthcare, particularly in AI-driven medical diagnostics, has raised critical concerns about data privacy in medical image classification. …”
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Privacy self-management and the issue of privacy externalities: of thwarted expectations, and harmful exploitation by Simeon de Brouwer

“…This term, related to similar concepts from the literature on privacy such as ‘networked privacy’ or ‘data pollution’, is used here to bring to light the incentives and exploitative dynamics behind a phenomenon which, I demonstrate, benefits both the user and the data controller to the detriment of third-party data subjects. …”
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Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD. by Yannick Marcon, Tom Bishop, Demetris Avraam, Xavier Escriba-Montagut, Patricia Ryser-Welch, Stuart Wheater, Paul Burton, Juan R González

“…DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. …”
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Secondary use under the European Health Data Space: setting the scene and towards a research agenda on privacy-enhancing technologies by Sarah van Drumpt, Kartik Chawla, Tom Barbereau, Dayana Spagnuelo, Linda van de Burgwal

Subjects: “…European Health Data Space…”
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Unique Method for Prognosis of Risk of Depressive Episodes Using Novel Measures to Model Uncertainty Under Data Privacy by Barbara Pękala, Dawid Kosior, Wojciech Rząsa, Katarzyna Garwol, Janusz Czuma

“…The computer support system designed for that purpose combines data privacy protection from various sources and uncertainty modeling, especially for incomplete data. …”
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A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos. by Shagun Sharma, Kalpna Guleria, Ayush Dogra, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…Additionally, it also has patient data-privacy concerns leading to anonymous information generalization, regulatory compliance issues, and data leakage challenges. …”
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Data structure and privacy protection analysis in big data environment based on blockchain technology by Yu Wang

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Enhancing privacy in clustering and data mining: A novel approach for sensitive data protection by Haythem Hayouni

“…However, these processes often involve the use of sensitive data, raising significant concerns about privacy, security, and trustworthiness. …”
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Right to privacy, Big Data and data protection: new challenges of the Colombian legal system by Dayron Dannylo Reyes Quintero, Margarita Rosa Lobo Contreras, Lucía Dayana Amaya Barbosa

“…Also, the perception and characteristics of Big Data are disclosed, trying to know how the rights involved in the materialization of the data analysis process can be protected; Finally, a critical sense is applied to the use of Big Data in modern political platforms, understanding the panorama for the Colombian legal system, which brings with it the probable violation of fundamental rights, such as personal privacy.…”
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Ethical Dilemmas and Coping Strategies in the Use of Psychological Scales in the Era of Big Data by Lirong LIU, Yanan ZHENG

“…Focusing on the ethical risks arising from the reconstruction of psychological scales due to innovations in big data technology, a systematic analysis is conducted on the research and current use of psychological scales within the context of big data. …”
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Knowledge Distillation for Face Recognition Using Synthetic Data With Dynamic Latent Sampling by Hatef Otroshi Shahreza, Anjith George, Sebastien Marcel

“…We propose a new framework (called SynthDistill) to train lightweight face recognition models by distilling the knowledge from a pretrained teacher model using synthetic data. We generate synthetic face images without identity labels, mitigating the problems in the intra-class variation generation of synthetic datasets, and dynamically sample from the intermediate latent space of a face generator network to generate new variations of the challenging images while further exploring new face images. …”
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Comprehensive Review of Privacy, Utility, and Fairness Offered by Synthetic Data by A. Kiran, P. Rubini, S. Saravana Kumar

“…First and foremost, how well synthetic data can preserve privacy and control disclosure, second is how good is its utility, and third, are they able to give fair results without any bias when used in machine learning. …”
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Protecting Location Privacy for Outsourced Spatial Data in Cloud Storage by Feng Tian, Xiaolin Gui, Jian An, Pan Yang, Jianqiang Zhao, Xuejun Zhang

“…As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. …”
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