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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Secure weighted possibilistic c-means algorithm on cloud for clustering big data
ترجمه فارسی عنوان مقاله:
الگوریتم C-Measure امکان سنجی امن در ابر برای خوشه بندی داده های بزرگ
منبع:
Sciencedirect - Elsevier - Information Sciences, Corrected proof: doi:10:1016/j:ins:2018:02:013
نویسنده:
Qingchen Zhang a,b, Laurence T. Yang a,b,∗, Arcangelo Castiglione c, Zhikui Chen d, Peng Li d
چکیده انگلیسی:
The weighted possibilistic c-means algorithm is an important soft clustering technique for
big data analytics with cloud computing. However, the private data will be disclosed when
the raw data is directly uploaded to cloud for efficient clustering. In this paper, a secure
weighted possibilistic c-means algorithm based on the BGV encryption scheme is proposed
for big data clustering on cloud. Specially, BGV is used to encrypt the raw data for the
privacy preservation on cloud. Furthermore, the Taylor theorem is used to approximate the
functions for calculating the weight value of each object and updating the membership
matrix and the cluster centers as the polynomial functions which only include addition
and multiplication operations such that the weighed possibilistic c-means algorithm can
be securely and correctly performed on the encrypted data in cloud. Finally, the presented
scheme is estimated on two big datasets, i.e., eGSAD and sWSN, by comparing with the
traditional weighted possibilistic c-means method in terms of effectiveness, efficiency and
scalability. The results show that the presented scheme performs more efficiently than the
traditional weighted possiblistic c-means algorithm and it achieves a good scalability on
cloud for big data clustering.
Keywords: Big data ، Possibilistic c-means algorithm ، Cloud computing ، BGV
قیمت: رایگان
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