Derin Öğrenmede Diferansiyel Mahremiyet

Year 2020, , 1 - 16, 30.06.2020

Yavuz Canbay , Şeref Sağıroğlu

https://doi.org/10.18640/ubgmd.750310

Cited By: 3

Abstract

Verinin boyut ve çeşitlilik olarak arttığı, kişisel verilerin kolaylıkla paylaşıldığı ve ihlallerinin sayısının hızla yükseldiği günümüzde veri mahremiyeti, üzerinde çokça çalışılan ve önlemler geliştirilen konuların başında gelmektedir. Kişisel verileri kullanan, depolayan veya işleyen her türlü uygulama, ürün veya sistem, veri mahremiyetini sağlamak, korumak ve doğru bir şekilde uygulandığını göstermek zorundadır. Son yıllarda veri mahremiyeti kapsamında pek çok yeni çözümler geliştirilse de teknolojik gelişmeler, yapay zekâdaki ilerlemeler, derin öğrenme yaklaşımlarının uygulama başarısı, bu yaklaşımların pek çok alanda kullanılmaya başlanması ve yapısı itibariyle kara-kutu çözüm sağlaması, veri mahremiyeti açısından yeni endişeleri de beraberinde getirmiştir. Bu çalışmada, günümüzün önemli yapay zekâ teknolojilerinden biri olan derin öğrenmede, kişisel bilgi içeren verilerin analiz edilmesi sürecinde mahremiyet koruyucu çeşitli önlemler incelenmiş, bu önlemlerden en çok kullanılanı olan diferansiyel mahremiyet açıklanmış ve derin öğrenmedeki uygulamaları ve tehditler karşılaştırılmıştır. Sunulan bu çalışmanın, kişisel verileri işleyen derin öğrenme tabanlı uygulamalarda, oluşabilecek ihlallerin önlenmesine, karşılaşılabilecek risklerin doğru belirlenmesine ve gereken önlemlerin daha sağlıklı alınmasına katkı sağlayacağı değerlendirilmektedir.

Keywords

derin öğrenme, diferansiyel mahremiyet, veri mahremiyeti

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