Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes
نویسندگان
چکیده
منابع مشابه
Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes
An enormous quantity of personal health information is available in recent decades and tampering of any part of this information imposes a great risk to the health care field. Existing anonymization methods are only apt for single sensitive and low dimensional data to keep up with privacy specifically like generalization and bucketization. In this paper, an anonymization technique is proposed t...
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Aiming at ensuring privacy preservation in personal data publishing, the topic of anonymization has been intensively studied in recent years. However, existing anonymization techniques all assume each tuple in the microdata table contains one single sensitive attribute (the SSA case), while none paid attention to the case of multiple sensitive attributes in a tuple (the MSA case). In this paper...
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هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...
15 صفحه اولSlicing : A Efficient Method For Privacy Preservation In Data Publishing
In this paper we propose and prove a new technique called “Overlapping Slicing” for privacy preservation of high dimensional data. The process of publishing the data in the web, faces many challenges today. The data usually contains the personal information which are personally identifiable to anyone, thus poses the problem of Privacy. Privacy is an important issue in data publishing. Many orga...
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The identity of patients must be protected when patient data are shared. The two most commonly used models to protect identity of patients are L-diversity and K-anonymity. However, existing work mainly considers data sets with a single sensitive attribute, while patient data often contain multiple sensitive attributes (e.g., diagnosis and treatment). This article shows that although the K-anony...
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ژورنال
عنوان ژورنال: SpringerPlus
سال: 2016
ISSN: 2193-1801
DOI: 10.1186/s40064-016-2490-0