کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6853886 | 1437278 | 2018 | 26 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Privacy-preserving collaborative fuzzy clustering
ترجمه فارسی عنوان
خواندن فازی مشارکتی حفظ حریم خصوصی
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کلمات کلیدی
سنجش مشارکتی، یادگیری مشارکتی، خوشه بندی حفظ حریم خصوصی، تصادف،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
In this paper, we address this challenge with a two-stage scheme called RG+RP: in the first stage, each participant perturbs his/her data by passing the data through a nonlinear function called repeated Gompertz (RG); in the second stage, he/she then projects his/her perturbed data to a lower dimension in an (almost) distance-preserving manner, using a specific random projection (RP) matrix. The nonlinear RG function is designed to mitigate maximum a posteriori (MAP) estimation attacks, while random projection resists independent component analysis (ICA) attacks and ensures clustering accuracy. The proposed two-stage randomisation scheme is assessed in terms of its recovery resistance to MAP estimation attacks. Preliminary theoretical analysis as well as experimental results on synthetic and real-world datasets indicate that RG+RP has better recovery resistance to MAP estimation attacks than most state-of-the-art techniques. For clustering, fuzzy c-means (FCM) is used. Results using seven cluster validity indices, root mean squared error (RMSE) and accuracy ratio show that clustering results based on two-stage-perturbed data are comparable to the clustering results based on raw data - this confirms the utility of our privacy-preserving scheme when used with either FCM or HCM.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Data & Knowledge Engineering - Volume 116, July 2018, Pages 21-41
Journal: Data & Knowledge Engineering - Volume 116, July 2018, Pages 21-41
نویسندگان
Lingjuan Lyu, James C. Bezdek, Yee Wei Law, Xuanli He, Marimuthu Palaniswami,