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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
A new technique ensuring privacy in big data: K-anonymity without prior value of the threshold k
ترجمه فارسی عنوان مقاله:
یک تکنیک جدید مطمعن حریم خصوصی در داده های بزرگ: K-anonymity بدون مقدار قبلی آستانه k
منبع:
Sciencedirect - Elsevier - Procedia Computer Science, 127 (2018) 52-59: doi:10:1016/j:procs:2018:01:097
نویسنده:
Zakariae El Ouazzania,* and Hanan El Bakkalia
چکیده انگلیسی:
Big data has become omnipresent and crucial for many application domains. Big data makes reference to the explosive quantity
of data generated in today’s society that might contain personally identifiable information (PII). That’s why the challenge from
the point of view of data privacy is one of the major hurdles for the application of big data. In that situation, several techniques
were exposed in order to ensure privacy in big data including generalization, randomization and cryptographic techniques as
well. It is well known that there exist two main types of attributes in the literature, quasi identifier and sensitive attributes. In this
paper, we are going to focus on quasi identifier attributes. Over the years, k-anonymity has been treated with great interest as an
anonymization technique ensuring privacy in big data when we are dealing with quasi identifier attributes. Despite the fact that
many algorithms of k-anonymity have been proposed, most of them admit that the threshold k of k-anonymity has to be known
before anonymizing the data set. Here, a novel way in applying k-anonymity for quasi identifier attributes is presented. It’s a new
algorithm called “k-anonymity without prior value of the threshold k”. Our proposed algorithm was experimentally evaluated
using a test table of quasi identifier attributes. Furthermore, we highlight all the steps of our proposed algorithm with detailed
comments.
Keywords: k-anonymity; quasi identifier attributes; big data; anonymization; privacy
قیمت: رایگان
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