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Local Differential Privacy-Based Federated Learning for Internet of Things [PDF]
IEEE Internet of Things Journal, 2020 The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc.
Yang Zhao +7 more
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Per-instance Differential Privacy
The Journal of Privacy and Confidentiality, 2019 We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set.
Yu-Xiang Wang
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Differential Privacy in Practice: Expose your Epsilons!
The Journal of Privacy and Confidentiality, 2019 Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn ...
Cynthia Dwork +2 more
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FL-ODP: An Optimized Differential Privacy Enabled Privacy Preserving Federated Learning
IEEE Access, 2023 Privacy-preserving methods and techniques aim to safeguard the privacy of individuals and groups while facilitating data sharing for specific purposes.
Maria Iqbal +4 more
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The Protection of Data Sharing for Privacy in Financial Vision
Applied Sciences, 2022 The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems.
Yi-Ren Wang, Yun-Cheng Tsai
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Hierarchical Aggregation for Numerical Data under Local Differential Privacy
Sensors, 2023 The proposal of local differential privacy solves the problem that the data collector must be trusted in centralized differential privacy models. The statistical analysis of numerical data under local differential privacy has been widely studied by many ...
Mingchao Hao, Wanqing Wu, Yuan Wan
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Equitable differential privacy
Frontiers in Big DataDifferential privacy (DP) has been in the public spotlight since the announcement of its use in the 2020 U.S. Census. While DP algorithms have substantially improved the confidentiality protections provided to Census respondents, concerns have been ...
Vasundhara Kaul, Tamalika Mukherjee
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Differentially Private Mixed Data Release Algorithm Based on k-prototype Clustering
Jisuanji kexue yu tansuo, 2021 Differential privacy is a model that provides strong privacy protection. Under the non-interactive frame-work, data managers can publish data sets processed by differential privacy protection technology for researchers to conduct mining and analysis ...
QU Jingjing, CAI Ying, FAN Yanfang, XIA Hongke
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Survey on differential privacy and its progress
Tongxin xuebao, 2017 With the arrival of the era of big data sharing,data privacy protection issues will be highlighted.Since its introduction in 2006,differential privacy technology has been widely researched in data mining and data publishing.In recent years,Google,Apple ...
Zhi-qiang GAO, Yu-tao WANG
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Privacy-Preserving Bin-Packing With Differential Privacy
IEEE Open Journal of Signal Processing, 2022 With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service.
Tianyu Li +2 more
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