Privacy-preserving federated mining of frequent itemsets

Chen, Yao; Gan, Wensheng; Wu, Yongdong; Yu, Philip S.

doi:10.1016/j.ins.2023.01.002

Cited by 12 publications

(2 citation statements)

References 55 publications

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“…FL is a distributed machine learning paradigm with a privacy protection function, designed for a large number of edge devices [12]. FL allows the original data to be retained on each edge device and uses the data, computing power, and model-building capabilities of each edge device to perform tasks [13,14]. The server in FL collects and aggregates the parameters or models sent by the edge devices.…”

Section: Epic Games Unitysoftware Incmentioning

confidence: 99%

Federated Learning for Metaverse: A Survey

Chen

Huang

Gan

et al. 2023

Companion Proceedings of the ACM Web Conference 2023

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The metaverse, which is at the stage of innovation and exploration, faces the dilemma of data collection and the problem of private data leakage in the process of development. This can seriously hinder the widespread deployment of the metaverse. Fortunately, federated learning (FL) is a solution to the above problems. FL is a distributed machine learning paradigm with privacy-preserving features designed for a large number of edge devices. Federated learning for metaverse (FL4M) will be a powerful tool. Because FL allows edge devices to participate in training tasks locally using their own data, computational power, and model-building capabilities. Applying FL to the metaverse not only protects the data privacy of participants but also reduces the need for high computing power and high memory on servers. Until now, there have been many studies about FL and the metaverse, respectively. In this paper, we review some of the early advances of FL4M, which will be a research direction with unlimited development potential. We first introduce the concepts of metaverse and FL, respectively. Besides, we discuss the convergence of key metaverse technologies and FL in detail, such as big data, communication technology, the Internet of Things, edge computing, blockchain, and extended reality. Finally, we discuss some key challenges and promising directions of FL4M in detail. In summary, we hope that our up-to-date brief survey can help people better understand FL4M and build a fair, open, and secure metaverse. CCS CONCEPTS• Computing methodologies → Federated learning.

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Section: Epic Games Unitysoftware Incmentioning

confidence: 99%

Federated Learning for Metaverse: A Survey

Chen

Huang

Gan

et al. 2023

Companion Proceedings of the ACM Web Conference 2023

Self Cite

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“…In recent years, big data technologies have emerged in the scenario of booming network and information technology. People can discover the laws and knowledge hidden in the data from the huge amount of data through data mining algorithms, which is important for industrial development, social services, and many other fields (Chen et al, 2023). If the data is directly provided to a third party, it will lead to the leakage of personal privacy information, which will bring a great threat to personal safety and property security.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Personalized Randomized Response Method Based on Local Differential Privacy

Zhang,

Zhang

et al. 2024

International Journal of Information Security and Privacy

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Aiming at the problem of adopting the same level of privacy protection for sensitive data in the process of data collection and ignoring the difference in privacy protection requirements, the authors propose an adaptive personalized randomized response method based on local differential privacy (LDP-APRR). LDP-APRR determines the sensitive level through the user scoring strategy, introduces the concept of sensitive weights for adaptive allocation of privacy budget, and realizes the personalized privacy protection of sensitive attributes and attribute values. To verify the distorted data availability, LDP-APRR is applied to frequent items mining scenarios and compared with mining associations with secrecy konstraints (MASK), and grouping-based randomization for privacy-preserving frequent pattern mining (GR-PPFM). Results show that the LDP-APRR achieves personalized protection of sensitive attributes and attribute values with user participation, and the maxPrivacy and avgPrivacy are improved by 1.2% and 4.3%, respectively, while the availability of distorted data is guaranteed.

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