Privacy-Conducive Data Ecosystem Architecture: By-Design Vulnerability Assessment Using Privacy Risk Expansion Factor and Privacy Exposure Index by Ionela Chereja, Rudolf Erdei, Daniela Delinschi, Emil Pasca, Anca Avram, Oliviu Matei

“…The increasing complexity of data ecosystems demands advanced methodologies for systematic privacy risk assessment. …”
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Solving Truthfulness-Privacy Trade-Off in Mixed Data Outsourcing by Using Data Balancing and Attribute Correlation-Aware Differential Privacy by Abdul Majeed, Seong Oun Hwang

“…Anonymization methods are mostly used in data outsourcing to protect privacy; however, it is very hard to apply anonymity to datasets of poor quality while maintaining an equilibrium between privacy, utility, and truthfulness (i.e., ensuring the values in anonymized data are consistent with the real data). …”
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Survey of split learning data privacy by QIN Yiqun, MA Xiaojing, FU Jiayun, HU Pingyi, XU Peng, JIN Hai

“…In response to these concerns, the Personal Information Protection Law of the People's Republic of China was promulgated to regulate the collection, use, and transmission of private information. Despite this, machine learning requires a large amount of data, necessitating the development of privacy protection technologies that allow for the collection and processing of data under legal and compliant conditions. …”
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Can Synthetic Data Protect Privacy? by Gidan Min, Junhyoung Oh

“…To systematically evaluate the privacy protection performance of synthetic data generation algorithms (Synthpop, CTGAN, RTVAE, TVAE, DataSynthesizer), this study applied various safety metrics. …”
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Towards a privacy debt by Xabier Larrucea, Izaskun Santamaría, Manuel Graña Romay

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Adaptive personalized privacy-preserving data collection scheme with local differential privacy by Haina Song, Hua Shen, Nan Zhao, Zhangqing He, Wei Xiong, Minghu Wu, Mingwu Zhang

“…Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. …”
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An accuracy-privacy optimization framework considering user’s privacy requirements for data stream mining by Waruni Hewage, R. Sinha, M. Asif Naeem

“…Additionally, a data fitting module using kernel regression is integrated, a unique approach that predicts accuracy levels based on user-defined privacy thresholds. …”
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Sharing Privacy Protected and Statistically Sound Clinical Research Data Using Outsourced Data Storage by Geontae Noh, Ji Young Chun, Ik Rae Jeong

“…Researchers are able to obtain valuable information that they would not otherwise be able to access; however, privacy concerns arise when sharing clinical data in these outsourced publicly available data storage services. …”
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Data Anonymization and Privacy Preservation in Healthcare Systems by Datta Krishnasheesh, Gupta Himanshu

“…With the objective to identify key trends and driving factors, a bibliometric analysis was performed using VOSviewer, which focuses on prominent authors, dominant research topics, and collaborative networks in data privacy. …”
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Data Privacy Protection in Islamic Communication Perspective by Musfiah Saidah, Hilya Maylaffayza

“…Technology in the era of society 5.0 was rising to facilitate human work, but it is used to misuse big data from the individual level. …”
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Privacy-Preserving Data Sharing in Telehealth Services by Ammar Odeh, Eman Abdelfattah, Walid Salameh

“…Telehealth services, while providing immense benefits in terms of accessibility and efficiency, introduce complex challenges in maintaining data privacy and security. This paper proposes a privacy-preserving framework for secure data sharing within telehealth services, employing blockchain technology and advanced cryptographic techniques. …”
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Crowdsourced data leaking user's privacy while using anonymization technique by Naadiya Mirbahar Mirbahar, Kamlesh Kumar, Asif Ali Laghari, Mansoor Ahmed Khuhro

“…However, this may pose privacy risks to data owners, even though the data collectors usually anonymize the data before crowdsourcing. …”
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Addressing contemporary threats in anonymised healthcare data using privacy engineering by Sanjiv M. Narayan, Nitin Kohli, Megan M. Martin

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Unpacking Youth Privacy Management in AI Systems: A Privacy Calculus Model Analysis by Austin Shouli, Ankur Barthwal, Molly Campbell, Ajay Kumar Shrestha

“…The increasing use of Artificial Intelligence (AI) in daily life has introduced substantial issues in protecting user privacy, particularly for young digital citizens. …”
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Blockchain-enabled data governance for privacy-preserved sharing of confidential data by Jingchi Zhang, Anwitaman Datta

“…Our novel ABE encryption system supports multi-authority use cases while hiding access policy and ensuring identity privacy, which also protects data sharing against corrupt authorities. …”
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Combining observational and experimental data for causal inference considering data privacy by Mann Charlotte Z., Sales Adam C., Gagnon-Bartsch Johann A.

“…However, many observational datasets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. …”
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Privacy in the Internet: Myth or reality by Mikarić Bratislav, Marković Marija, Trajković Dušan

“…The present time, unthinkable without using Internet - from e-mail, through social networks, cloud services, GPS, to YouTube and mobile computing in business, as well as on a private level, poses a question: Is there a way to protect data and their privacy on the Internet? …”
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Towards Privacy-Aware and Trustworthy Data Sharing Using Blockchain for Edge Intelligence by Youyang Qu, Lichuan Ma, Wenjie Ye, Xuemeng Zhai, Shui Yu, Yunfeng Li, David Smith

“…Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. …”
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Design of Privacy Preservation Model for Data Stream Using Condensation Based Anonymization by Latha P, Thangaraj M

“…The condensation method minimizes lost information throughout the anonymization process in an effort to maintain the statistical features of the data. Using generalization and suppression approaches, k-Anonymity obscures identifying information in datasets, making it a useful tool for protecting individual privacy. …”
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Privacy-preserving recommender system using the data collaboration analysis for distributed datasets. by Tomoya Yanagi, Shunnosuke Ikeda, Noriyoshi Sukegawa, Yuichi Takano

“…To this end, we establish a framework for privacy-preserving recommender systems using the data collaboration analysis of distributed datasets. …”
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