Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması

Gülsüm Yiğit; Ayşe Kale

doi:10.29130/dubited.864635

TR EN

Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması

Abstract

Son dönemlerde derin öğrenmedeki devrim niteliğindeki gelişmeler ile birlikte yapay zekaya yönelik beklentiler gün geçtikçe artmaktadır. Konuşma tanıma, doğal dil işleme (NLP), görüntü işleme gibi birçok alanda etkin bir şekilde uygulanabilen bir araştırma alanı olan derin öğrenme klasik makine öğrenmesi ile karşılaştırıldığında daha yüksek başarı göstermektedir. Derin öğrenme ile geliştirilen modellerde eğitim ve tahminleme sırasında büyük miktarda veri kullanılmakta ve kullanılan veriler kişisel verilerden oluşabilmektedir. Bu verilerin işlenmesi sırasında kişisel verilerin korunması kanununa (KVKK) aykırı olmaması oldukça önemlidir. Bu nedenle verilerin gizliliği ve güvenliğinin sağlanması oldukça önemli bir husustur. Bu çalışmada, derin öğrenme modelleri geliştirilirken yaygın kullanılan mimariler verilmiştir. Verilerin gizliliği ve güvenliğini artırmak için literatürde yaygın olarak karşılaşılan güvenli çok partili hesaplama, diferansiyel mahremiyet, garbled devre protokolü ve homomorfik şifreleme araçları özetlenmiştir. Çeşitli sistem tasarımlarında kullanılan bu araçların yer aldığı güncel çalışmalar taranmıştır. Bu çalışmalar, derin öğrenme modelinin eğitim ve tahminleme aşamasında olmak üzere iki kategoride incelenmiştir. Literatürdeki çeşitli modeller üzerinde uygulanabilen güncel saldırılar ve bu saldırılardan korunmak amacıyla geliştirilen yöntemler verilmiştir. Ayrıca, güncel araştırma alanları belirlenmiştir. Buna göre, gelecekteki araştırma yönü kriptografik temelli yöntemlerin karmaşıklığının azaltılması ve geliştirilen modelin güvenilirliğini belirlemek için çeşitli ölçme ve değerlendirme yöntemlerinin geliştirilmesi yönünde olabilir.

Keywords

Derin Öğrenme, Güvenli Çok Partili Hesaplama, Diferansiyel Mahremiyet, Homomorfik Şifreleme, Garbled Devreler Protokolü

A Review Study on Privacy and Security in Deep Learning Models

Abstract

With the advanced progress in deep learning in recent times, expectations of artificial intelligence are increasing day by day. Deep Learning, a research area, applied effectively in many areas such as speech recognition, Natural Language Processing (NLP), and image processing, shows higher success than classical machine learning algorithms. Large amounts of data are employed during the training and prediction stages of deep learning models, in which the data may consist of the user's sensitive data. Therefore, it mustn't contradict the principle of protecting personal data while training and predicting. Consequently, it is crucial to ensure the privacy and security of data. In this study, various deep learning architectures are described. The widely used cryptographic tools applied to preserve the privacy and security of data are summarized. These are homomorphic encryption, differential privacy, secure multi-party computation, and garbled circuits. Recent works considering the privacy and security of deep learning models are examined under two different categories: train stage and prediction stage. In addition, the attacks against deep learning models and techniques for protection against these attacks are reviewed. Finally, open research areas are determined. Reducing the complexity of cryptographic-based models and developing evaluation methods to determine the model's privacy and security stand out as future research areas.

Keywords

Deep learning, Secure multiparty computation, Differential privacy, Homomorphic encryption, Garbled circuits protocol

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Details

Primary Language

Turkish

Subjects

Engineering

Journal Section

Review

Authors

Gülsüm Yiğit ^*
0000-0001-7010-169X
Türkiye

Ayşe Kale
0000-0002-9435-4584
Türkiye

Publication Date

October 31, 2021

Submission Date

January 19, 2021

Acceptance Date

July 28, 2021

Published in Issue

Year 2021 Volume: 9 Number: 5

DOI

https://doi.org/10.29130/dubited.864635

IZ

https://izlik.org/JA72PC47PK

APA

Yiğit, G., & Kale, A. (2021). Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması. Duzce University Journal of Science and Technology, 9(5), 1843-1859. https://doi.org/10.29130/dubited.864635

AMA

1.Yiğit G, Kale A. Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması. DUBİTED. 2021;9(5):1843-1859. doi:10.29130/dubited.864635

Chicago

Yiğit, Gülsüm, and Ayşe Kale. 2021. “Derin Öğrenme Modellerinde Mahremiyet Ve Güvenlik Üzerine Bir Derleme Çalışması”. Duzce University Journal of Science and Technology 9 (5): 1843-59. https://doi.org/10.29130/dubited.864635.

EndNote

Yiğit G, Kale A (October 1, 2021) Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması. Duzce University Journal of Science and Technology 9 5 1843–1859.

IEEE

[1]G. Yiğit and A. Kale, “Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması”, DUBİTED, vol. 9, no. 5, pp. 1843–1859, Oct. 2021, doi: 10.29130/dubited.864635.

ISNAD

Yiğit, Gülsüm - Kale, Ayşe. “Derin Öğrenme Modellerinde Mahremiyet Ve Güvenlik Üzerine Bir Derleme Çalışması”. Duzce University Journal of Science and Technology 9/5 (October 1, 2021): 1843-1859. https://doi.org/10.29130/dubited.864635.

JAMA

1.Yiğit G, Kale A. Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması. DUBİTED. 2021;9:1843–1859.

MLA

Yiğit, Gülsüm, and Ayşe Kale. “Derin Öğrenme Modellerinde Mahremiyet Ve Güvenlik Üzerine Bir Derleme Çalışması”. Duzce University Journal of Science and Technology, vol. 9, no. 5, Oct. 2021, pp. 1843-59, doi:10.29130/dubited.864635.

Vancouver

1.Gülsüm Yiğit, Ayşe Kale. Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması. DUBİTED. 2021 Oct. 1;9(5):1843-59. doi:10.29130/dubited.864635

Derin Öğrenme Modellerinde Mahremiyet ve Güvenlik Üzerine Bir Derleme Çalışması

Abstract

Keywords

A Review Study on Privacy and Security in Deep Learning Models

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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