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Anmin Fu

Researcher at Nanjing University of Science and Technology

Publications -  103
Citations -  1547

Anmin Fu is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 16, co-authored 79 publications receiving 858 citations. Previous affiliations of Anmin Fu include Nanjing University of Posts and Telecommunications & Xidian University.

Papers
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Privacy-Preserving Federated Learning in Fog Computing

TL;DR: This article proposes a privacy-preserving federated learning scheme in fog computing that can not only guarantee both data security and model security but completely resist collusion attacks launched by multiple malicious entities.
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NPP: A New Privacy-Aware Public Auditing Scheme for Cloud Data Sharing with Group Users

TL;DR: This paper proposes a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature that eliminates the abuse of single-authority power and provides non-frameability.
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VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT

TL;DR: This article proposes the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT that uses Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients.
Posted Content

Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review.

TL;DR: This work provides the community with a timely comprehensive review of backdoor attacks and countermeasures on deep learning, and presents key areas for future research on the backdoor, such as empirical security evaluations from physical trigger attacks, and more efficient and practical countermeasures are solicited.
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Data integrity verification of the outsourced big data in the cloud environment: A survey

TL;DR: A summary of the existing literature on data integrity verification (DIV) is collated, aiming to present a solid and stimulating review of current academic achievements for interested readers.