Conceptual Model Suggestions for Privacy Preserving Big Data Publishing

Year 2020, Volume: 23 Issue: 3, 785 - 798, 01.09.2020

Yavuz Canbay Yılmaz Vural Şeref Sağıroğlu

https://doi.org/10.2339/politeknik.535184

Cited By: 3

Abstract

Recent developments in IT has increased the speed of
data production and processing, as a result, big data concept with components
such as volume, velocity, variety and value has emerged. In order to get more
benefit from big data, it is necessary to share or publish the data by
preserving or respecting privacy. The literature reviews report that there is
no model that facilitates publishing big data by preserving privacy. Designing
Privacy Preserving Big Data Publishing (PPBDP) models is important to direct
all the parties and to meet the requirements of them correctly, and to create
the right infrastructures and services. In addition, it is necessary to consider
some factors such as cost and security when designing these models.

In this study, privacy preserving data publishing
models were reviewed, compared based on various criteria and then evaluated based
on privacy risk levels. Finally, big data architecture based new conceptual models
were then established for the first time according to these evaluations and privacy
risk levels. It is expected that the proposed models might contribute to the
literature on some issues, such as publishing big data with preserving privacy,
minimizing privacy risks and obtaining maximum benefit from the big data.

Keywords

Privacy preserving, big data, privacy preserving big data publishing models, conceptual model

Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri

Abstract

Teknolojinin
gelişmesi ile beraber veri üretim ve işleme hızı artmış, bunun sonucu olarak
hacim, hız, çeşitlilik ve değer gibi bileşenlere sahip büyük veri kavramı
ortaya çıkmıştır. Büyük verilerden elde edilecek faydayı arttırmak için bu
verilerin mahremiyetini koruyarak paylaşmak veya yayınlamak gerekir. Literatür
incelendiğinde, büyük verinin mahremiyetini koruyarak yayınlanmasını kolaylaştıran
herhangi bir modelin olmadığı tespit edilmiştir. Mahremiyet Korumalı Büyük Veri
Yayınlama (Privacy Preserving Big Data Publishing – PPBDP) modellerinin
oluşturulması, büyük veri mahremiyeti koruma sürecindeki tüm tarafların doğru
bir şekilde yönlendirilmesi ve gereksinimlerinin doğru karşılanması, doğru alt
yapı ve hizmetlerin oluşturulması adına önemlidir. Ayrıca, bu modelleri
oluştururken maliyet ve güvenlik gibi faktörleri de göz önünde bulundurmak
gerekir.   

Bu çalışmada,
mahremiyet korumalı geleneksel veri yayınlama modelleri araştırılmış, çeşitli
kriterlere göre karşılaştırılarak mahremiyet risk seviyeleri değerlendirilmiş
ve bu risk seviyelerini de dikkate alan büyük veri temelli yeni kavramsal modeller
ilk defa önerilmiştir. Önerilen bu modeller senaryo temelli olarak
oluşturulmuş, üstünlükleri ve dezavantajları sunulmuştur. Önerilen modellerin,
büyük verilerin mahremiyetinin korunarak yayınlanması, mahremiyet risklerinin
minimize edilmesi ve büyük veriden maksimum faydanın sağlanması gibi pek çok konuda
katkılar sağlayacağı değerlendirilmektedir.  

Keywords

mahremiyet koruma, büyük veri, mahremiyet korumalı büyük veri yayınlama modelleri, kavramsal model

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