کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4955394 1444213 2018 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Privacy-preserving tabular data publishing: A comprehensive evaluation from web to cloud
ترجمه فارسی عنوان
انتشار داده های جدول محرمانگی حفظ حریم خصوصی: ارزیابی جامع از وب به ابر
کلمات کلیدی
حریم خصوصی داده ها، انتشار اطلاعات با حفظ حریم خصوصی، شناسایی داده ها، جریان داده ها، خصوصیات چندگانه، خصوصیات حساس تک،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی

The amount of data collected by various organizations about individuals is continuously increasing. This includes diverse data sources often for data of high dimensionality. Most of these data are stored in tabular format and can include sensitive content. Preserving data privacy is an essential task in order to allow such data to be published for different research and analysis purposes. In this context, Privacy-Preserving Tabular Data Publishing (PPTDP) has drawn considerable attention, where different approaches have been proposed to preserve the privacy of individuals' tabular data. Such data can include Single Sensitive Attributes (SSA) or Multiple Sensitive Attributes (MSA) or come from data streams. In this paper, we conduct a comprehensive study to analyze and evaluate the main different data anonymization approaches that have been introduced in PPTDP. The study investigates the three broad areas of research: SSA, MSA and data streams. A detailed criticism is presented to highlight the strengths and the weaknesses of each approach including their deployment in the cloud and Internet of Things (IoT) environments. A research gap analysis is discussed with a focus on capturing current state of the art in this field in order to highlight the future directions that can be considered.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Security - Volume 72, January 2018, Pages 74-95
نویسندگان
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