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- Security and privacy in 6G networks: New areas and new challenges363
This survey discusses four key aspects of 6G networks – real-time intelligent edge computing, distributed artificial intelligence, intelligent radio, and 3D intercoms – and some promising emerging technologies in each area, along with the relevant security and privacy issues.
- Differentially Private Data Publishing and Analysis: A Survey330
This survey compares the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy.
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- A blockchain-based location privacy-preserving crowdsensing system170
This paper identifies three ways locations can be disclosed in traditional crowdsensing systems and proposes a novel solution, dubbed a blockchain privacy-preservation crowdsensing system, to address these privacy problems.
- Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity162
The literature to date on adversarial attacks and their countermeasures in the realm of deep learning is reviewed and the first analysis framework for a systematic understanding is offered, built from the perspective of cybersecurity to provide a lifecycle for adversarial attacked and defenses.
- Machine Unlearning: A Survey156
The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category’s advantages and limitations.
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- Location Privacy and Its Applications: A Systematic Study114
This in-depth analysis of the current state-of-play in location privacy is designed to provide a solid foundation for future studies in the field.
- Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff110
A detailed literature review of privacy and fairness issues is presented, highlighting unique challenges posed by FL and solutions in federated settings and pointing out new research directions in fair and private FL.
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- Correlated Differential Privacy: Feature Selection in Machine Learning95
A correlation reduction scheme with differentially private feature selection considering the issue of privacy loss when data have correlation in machine learning tasks is proposed, which can be widely used in machineLearning algorithms, which provide services in industrial areas.
- Differential Privacy and Applications91
This chapter presents three methods that apply differential privacy to achieve location privacy for LBSs: the geo-indistinguishability method, the synthetic differentially private trajectory Publishing method, and the hierarchical location data publishing method, with an emphasis on the last one.
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- Machine Learning Differential Privacy With Multifunctional Aggregation in a Fog Computing Architecture68
This paper proposes a multifunctional data aggregation method with differential privacy that operates within a fog computing architecture, which extends cloud computing to the edge of the network, alleviating much of the computational burden on the cloud server.
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- Privacy Intelligence: A Survey on Image Privacy in Online Social Networks66
A survey of “privacy intelligence” that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective and presents the important properties and a taxonomy ofOSN image privacy, along with a high-level privacy analysis framework based on the lifecycle of OSn image sharing.
- An effective privacy preserving algorithm for neighborhood-based collaborative filtering66
The results from experiments show that the proposed PNCF algorithm can obtain a rigid privacy guarantee without high accuracy loss, and the proposed algorithm can resist a KNN attack while retaining the accuracy of recommendations.
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- Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack60
The experimental results show that the proposed trace removal attack can significantly compromise the detection accuracy of six state-of-the-art DeepFake detectors while causing only a negligible degradation in visual quality.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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