Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree by Shuai Jiang, Naoto Iwata, Sayaka Kamei, Kazi Md. Rokibul Alam, Yasuhiko Morimoto

“…Privacy preservation poses significant challenges in third-party data sharing, particularly when handling table data containing personal information such as demographic and behavioral records. …”
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Internet of things data security and privacy protection based on improved federated learning by Wang Gang

“…At the same time, using the decentralized function to encrypt the privacy of the training model, the learning model can provide more secure and reliable services, aiming to solve the problem of large difference in the quality of computing nodes and data privacy leakage in the current FL. …”
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Federated learning for privacy-enhanced mental health prediction with multimodal data integration by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…The proposed framework incorporates a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network within a federated learning environment, ensuring that raw user data remains decentralised and privacy is preserved using differential privacy and encryption techniques. …”
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DiffPRFs:random forest under differential privacy by Hai-rong MU, Li-ping DING, Yu-ning SONG, Guo-qing LU

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Legitimate Expectations of Privacy in the Era of Digitalization by E. Ostanina, E. Titova

“…This article contends that in the present era of digitalization people’s right to privacy should be protected no less than it was before the widespread use of digital technologies. …”
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Sharing is CAIRing: Characterizing principles and assessing properties of universal privacy evaluation for synthetic tabular data by Tobias Hyrup, Anton Danholt Lautrup, Arthur Zimek, Peter Schneider-Kamp

“…However, the ability to share data is hindered by regulations protecting the privacy of natural persons. …”
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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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Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption by William J. Buchanan, Hisham Ali

“…The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. …”
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Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms by V. Maheskumar, R. Vijayarajeswari

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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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Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques by Parihar Bhawana, Kiran Ajmeera, Valaboju Sabitha, Rashid Syed Zahidur, Liyakat Kazi Kutubuddin Sayyad, D R Anita Sofia Liz

“…Traditional solutions tend to use standard techniques like basic data wrapping and cryptographic 'rings'; but, due to the design properties required, they end up as lightweight mechanisms, usually not interpretation-at-all capable because of the need for protecting data during processing - leaving these applications hard to use and maintain long-term, or otherwise, limited to cloud computing and federated learning, when individual data types can be worked on within providers like AWS, Azure, etc, etc; or, even, explaining the results with near total indifference to the underlying big data tools, analytics, or neural architectures. …”
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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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My privacy at risk – my guard is on: a study of SNS use among young adults by Meenakshi Handa, Ronika Bhalla, Parul Ahuja

“…Purpose – Increasing incidents of privacy invasion on social networking sites (SNS) are intensifying the concerns among stakeholders about the misuse of personal data. …”
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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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Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets by Louis Philippe Sondeck, Maryline Laurent

“…This paper proposes a practical and ready-to-use methodology for re-identification risk assessment, the originality of which is manifold: (1) it is the first to follow well-known risk analysis methods (e.g. …”
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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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