Privacy enhanced matrix factorization for recommendation with local differential privacy by Shin, Hyejin, Kim, Sungwook, Shin, Junbum, Xiao, Xiaokui

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K-means clustering with local dᵪ-privacy for privacy-preserving data analysis by Yang, Mengmeng, Tjuawinata, Ivan, Lam, Kwok-Yan

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A tripartite model of trust in Facebook : acceptance of information personalization, privacy concern, and privacy literacy by Rosenthal, Sonny, Wasenden, Ole-Christian, Gronnevet, Gorm-Andreas, Ling, Rich

“…We argue that trust in Facebook is related to (1) trust in companies that collect personal data, (2) acceptance of information personalization, (3) low privacy concern, and (4) low privacy literacy. Further, we argue that privacy literacy amplifies the relationship between privacy concern and the other factors. …”
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Teens' privacy management on video-sharing social media: the roles of perceived privacy risk and parental mediation by Kang, Hyunjin, Shin, Wonsun, Huang, Junru

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Love at First Sight: The Interplay Between Privacy Dispositions and Privacy Calculus in Online Social Connectivity Management by Choi, Ben, Wu, Yi, Yu, Jie, Land, Lesley

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Decentralized detection with robust information privacy protection by Sun, Meng, Tay, Wee Peng

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Compressive privacy for a linear dynamical system by Song, Yang, Wang, Chong Xiao, Tay, Wee Peng

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Privacy and robustness in federated learning: attacks and defenses by Lyu, Lingjuan, Yu, Han, Ma, Xingjun, Chen, Chen, Sun, Lichao, Zhao, Jun, Yang, Qiang, Yu, Philip S.

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Functional mechanism : regression analysis under differential privacy by Winslett, Marianne, Zhang, Jun, Zhang, Zhenjie, Xiao, Xiaokui, Yang, Yin

“…ɛ-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. …”
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Privacy preserving user based web service recommendations by Ibrahim Khalil, Badsha, Shahriar, Yi, Xun, Liu, Dongxi, Nepal, Surya, Lam, Kwok-Yan

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Privacy-preserving user profile matching in social networks by Yi, Xun, Bertino, Elisa, Rao, Fang-Yu, Lam, Kwok-Yan, Nepal, Surya, Bouguettaya, Athman

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Privacy and security issues in deep learning : a survey by Liu, Ximeng, Xie, Lehui, Wang, Yaopeng, Zou, Jian, Xiong, Jinbo, Ying, Zuobin, Vasilakos, Athanasios V.

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Diminishing personal information privacy weakens image concerns by Riyanto, Yohanes Eko, Zhang, Jianlin

“…However, users' limited ability to control information spread could compromise privacy. People care about how others perceive them. …”
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Privacy-preserving aggregation in federated learning: a survey by Liu, Ziyao, Guo, Jiale, Yang, Wenzhuo, Fan, Jiani, Lam, Kwok-Yan, Zhao, Jun

“…Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. …”
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Privacy-aware and Security-enhanced efficient Matchmaking Encryption by Sun, Jianfei, Xu, Guowen, Zhang, Tianwei, Yang, Xuehuan, Alazab, Mamoun, Deng, Robert H. H.

“…In this paper, we invent PS-ME, the first Privacy-aware and Security-enhanced efficient Matchmaking Encryption (ME) for flexible data sharing. …”
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Topology-aware differential privacy for decentralized image classification by Guo, Shangwei, Zhang, Tianwei, Xu, Guowen, Yu, Han, Xiang, Tao, Liu, Yang

“…One common defense direction is to adopt Differential Privacy (DP) to secure the optimization algorithms such as Stochastic Gradient Descent (SGD). …”
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Local differential privacy-based federated learning for Internet of Things by Zhao, Yang, Zhao, Jun, Yang, Mengmeng, Wang, Teng, Wang, Ning, Lyu, Lingjuan, Niyato, Dusit, Lam, Kwok-Yan

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On the relationship between inference and data privacy in decentralized IoT networks by Sun, Meng, Tay, Wee Peng

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Privacy-preserving distributed projection LMS for linear multitask networks by Wang, Chengcheng, Tay, Wee Peng, Wei, Ye, Wang, Yuan

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Privacy-preserving blockchain-based federated learning for IoT devices by Zhao, Yang, Zhao, Jun, Jiang, Linshan, Tan, Rui, Niyato, Dusit, Li, Zengxiang, Lyu, Lingjuan, Liu, Yingbo

“…To protect customers' privacy and improve the test accuracy, we enforce differential privacy (DP) on the extracted features and propose a new normalization technique. …”
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