Yazar "Nergiz, Mehmet Ercan" seçeneğine göre listele

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    Öğe
    Differentially private nearest neighbor classification
    (Springer, 2017) Gursoy, Mehmet Emre; Inan, Ali; Nergiz, Mehmet Ercan; Saygin, Yucel
    Instance-based learning, and the k-nearest neighbors algorithm (k-NN) in particular, provide simple yet effective classification algorithms for data mining. Classifiers are often executed on sensitive information such as medical or personal data. Differential privacy has recently emerged as the accepted standard for privacy protection in sensitive data. However, straightforward applications of differential privacy to k-NN classification yield rather inaccurate results. Motivated by this, we develop algorithms to increase the accuracy of private instance-based classification. We first describe the radius neighbors classifier (r-N) and show that its accuracy under differential privacy can be greatly improved by a non-trivial sensitivity analysis. Then, for k-NN classification, we build algorithms that convert k-NN classifiers to r-N classifiers. We experimentally evaluate the accuracy of both classifiers using various datasets. Experiments show that our proposed classifiers significantly outperform baseline private classifiers (i.e., straightforward applications of differential privacy) and executing the classifiers on a dataset published using differential privacy. In addition, the accuracy of our proposed k-NN classifiers are at least comparable to, and in many cases better than, the other differentially private machine learning techniques.
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    Öğe
    Privacy-Preserving Learning Analytics: Challenges and Techniques
    (IEEE Computer Soc, 2017) Gursoy, Mehmet Emre; Inan, Ali; Nergiz, Mehmet Ercan; Saygin, Yucel
    Educational data contains valuable information that can be harvested through learning analytics to provide new insights for a better education system. However, sharing or analysis of this data introduce privacy risks for the data subjects, mostly students. Existing work in the learning analytics literature identifies the need for privacy and pose interesting research directions, but fails to apply state of the art privacy protection methods with quantifiable and mathematically rigorous privacy guarantees. This work aims to employ and evaluate such methods on learning analytics by approaching the problem from two perspectives: (1) the data is anonymized and then shared with a learning analytics expert, and (2) the learning analytics expert is given a privacy-preserving interface that governs her access to the data. We develop proof-of-concept implementations of privacy preserving learning analytics tasks using both perspectives and run them on real and synthetic datasets. We also present an experimental study on the trade-off between individuals' privacy and the accuracy of the learning analytics tasks.
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    Öğe
    Student Data Protection: A Technical Assessment in the Context of the Fatih Project
    (2017) İnan, Ali; Nergiz, Mehmet Ercan; Saygın, Yücel
    Mahremiyet temel bir insan hakkıdır ve 2010 tarihinde yapılan Anayasa değişikliğiyle Türkiye Cumhuriyeti vatandaşları için güvence altına alınmıştır. Mahremiyet, 7 Nisan 2016 tarihinde Resmi Gazete'de yayınlanan Kişisel Verilerin Korunması Kanunu ile korunmaktadır. Bu kanun ile beraber hem özel sektör hem de kamu kurumlarında veri koruması konusunun tartışmaya açılması beklenmektedir. Öte yandan ülkemizce eğitim alanında e-Okul ile başlayıp FATİH projesi ile çok daha kapsamlı hale gelen dijital dönüşüm projeleri halen devam etmektedir. Avrupa Birliği'nde bu tarz büyük çaplı projelere başlanmadan önce mahremiyet etki değerlendirmesi yapılması zorunludur. Projelerin tasarım ve uygulanma aşaması ise bu etki değerlendirmeleri dikkate alınarak gerçekleştirilmektedir. Bu makalenin amacı FATİH projesi göz önüne alınarak öğrenci verilerinin korunması ile ilgili teknik hususların değerlendirilmesidir. Bu bakımdan Türkiye'de yazarların bilgisi dahilinde ilk kez yapılan bu çalışmanın ileride farklı sektörlerde benzer çalışmalara vesile olup ışık tutması ümit edilmektedir.