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Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3013446
Abstract: At present, many enterprises provide users with better services by collecting their sensitive information. However, these enterprises will inevitably cause the leakage of users’ information, thereby infringing on users’ privacy. Local differential privacy resolves this…
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Keywords:
local differential;
problem;
privacy budget;
differential privacy ... See more keywords
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Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3033987
Abstract: Most of local differential privacy frameworks target statistics on certain privacy behaviors of users, but not behavior sequence. In this paper, we explore and propose a behavior sequence mining model that satisfies the local differential…
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Keywords:
local differential;
behavior sequence;
model;
differential privacy ... See more keywords
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Published in 2020 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2019.2952146
Abstract: The Internet of Things (IoT) is transforming major industries, including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations, such as the amalgamation of software-defined networks (SDNs)…
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Keywords:
local differential;
randomization module;
randomization;
privacy ... See more keywords
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Published in 2020 at "IEEE Intelligent Systems"
DOI: 10.1109/mis.2020.3010335
Abstract: The growing number of mobile and IoT devices has nourished many intelligent applications. In order to produce high-quality machine learning models, they constantly access and collect rich personal data such as photos, browsing history, and…
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Keywords:
local differential;
federated machine;
differential privacy;
privacy ... See more keywords
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Published in 2022 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2022.3170242
Abstract: Local Differential Privacy (LDP) is a popular standard for privacy-preserving data collection. Numerous LDP protocols have been proposed in the literature which differ in how they provide higher utility in different settings. Yet, few have…
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Keywords:
local differential;
privacy;
analysis;
differential privacy ... See more keywords
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Published in 2022 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2022.3198283
Abstract: Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profile. It is typical to collect…
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Keywords:
towards private;
learning decentralized;
local differential;
privacy ... See more keywords
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Published in 2023 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2023.3256124
Abstract: The existing solutions related to local differential privacy (LDP) in multi-layer networks for edge computing scenarios present several limitations in both key-value data heavy hitter identification and related frequency and mean estimation tasks. First, existing…
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Keywords:
value data;
multi layer;
local differential;
edge computing ... See more keywords
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Published in 2021 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2021.3108463
Abstract: The fast development of vehicle positioning technologies has paved the way for in-vehicle recommendation systems. Successive Point-of-Interest (POI) recommendation, one of the most common forms of in-vehicle recommendation, can help users choose places where they…
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Keywords:
local differential;
successive point;
point interest;
differential privacy ... See more keywords
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Published in 2017 at "Duke Mathematical Journal"
DOI: 10.1215/00127094-3715296
Abstract: A family of algebraic curves covering a projective variety $X$ is called a web of curves on $X$ if it has only finitely many members through a general point of $X$. A web of curves…
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Keywords:
differential geometry;
geometry;
algebraic curves;
web structure ... See more keywords
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Published in 2023 at "Entropy"
DOI: 10.3390/e25030485
Abstract: As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning…
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Keywords:
federated learning;
local differential;
privacy;
privacy protection ... See more keywords
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Published in 2022 at "Statistica Sinica"
DOI: 10.5705/ss.202022.0186
Abstract: We consider the problem of estimating a regression function from anonymized data in the framework of local differential privacy. We propose a novel partitioning estimate of the regression function, derive a rate of convergence for…
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Keywords:
differential privacy;
rate optimal;
regression;
local differential ... See more keywords