2022
DOI: 10.1109/tifs.2022.3189532
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K-Means Clustering With Local dᵪ-Privacy for Privacy-Preserving Data Analysis

Abstract: Privacy-preserving data analysis is an emerging area that addresses the dilemma of performing data analysis on user data while protecting users' privacy. In this paper, we consider the problem of constructing privacy-preserving K-means clustering protocol for data analysis that provides local privacy to users' data. To enable a desirable degree of local privacy guarantee while maintaining high accuracy of the clustering, we adopt a generalized differential privacy definition, dχ-privacy, which quantifies the d… Show more

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Cited by 19 publications
(6 citation statements)
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References 26 publications
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“…The test set contain 37611 trial pairs, and the task is to verify whether the trial pair of utterances are from the same speaker. We use MFA-Conformer model 5 to do the feature extraction. After obtaining the embedding feature vector of the input audio, the vector is encrypted and send to the private network.…”
Section: B Experiments On Hybrid Pe-nn Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…The test set contain 37611 trial pairs, and the task is to verify whether the trial pair of utterances are from the same speaker. We use MFA-Conformer model 5 to do the feature extraction. After obtaining the embedding feature vector of the input audio, the vector is encrypted and send to the private network.…”
Section: B Experiments On Hybrid Pe-nn Modelmentioning
confidence: 99%
“…Typically, AI models, such as deep neural networks (DNNs), apply a sequence of evaluations on the input data and model parameters to obtain an inference output. Many techniques are studied for privacy-preserving machine learning, such as differential privacy [4], [5] and federated learning [6]- [9]. These techniques address the concern of privacy issues of the training data and of the data sharing during model training.…”
Section: Introductionmentioning
confidence: 99%
“…References [21,22] focused on providing local privacy for users who participate in the K-means clustering analysis. The general idea of GoodCenters is utilizing some hash functions to map the original data records to another space where the collision happens with a high chance to users' data records being closer to each other and less chance to the data records far away from each other.…”
Section: K-means Clustering With Local Settingmentioning
confidence: 99%
“…Particularly, when agents engage in data transactions with external power and gas grids, the internal parameters of the agents become more susceptible to leakage. In order to safeguard data privacy in EH, we adopt an efficient and computationally simple approach known as local differential privacy [49][50][51][52][53][54]. This approach not only allows for quantifying the strength of privacy protection but also enables the application of the noise addition process at each EH node.…”
Section: Eh Privacy Protection Based On Differential Privacymentioning
confidence: 99%