Yörünge Verisi Yayınlamada Mahremiyet Duyarlı Yeni Bir Model Önerisi ve Uygulaması

Year 2021, Volume: 24 Issue: 3, 1275 - 1286, 01.09.2021

Murat Akın , Yavuz Canbay , Şeref Sağıroğlu

https://doi.org/10.2339/politeknik.916234

Abstract

Konum tabanlı servisler (KTS), sağladıkları bilgi ve yönlendirmeler ile gündelik hayatı kolaylaştırmaktadır. Kullanıcıların KTS’leri kullanarak gezinmesi sonucu elde edilen konum bilgileri zamana göre sıralandığında, yörünge verileri oluşmaktadır. Bu veriler, KTS sağlayıcıları tarafından toplanmakta, depolanmakta, işlenmekte ve çeşitli gerekçelerle yayınlanmaktadır. Yörünge verileri kişisel veri olarak değerlendirildiği için, bu tür veriler orijinal hali ile yayınlanırsa, saldırganlar kurbanları hakkında hassas bilgilere ulaşabilir ve ifşa saldırıları düzenleyebilir. Bu problemi gidermek için mahremiyet koruyucu güncel yaklaşımlara her zaman ihtiyaç vardır. Bu çalışmada, yörünge verilerinin mahremiyetini koruyarak yayınlanmasını sağlamak için diferansiyel mahremiyet tabanlı yeni bir anonimleştirme modeli önerilmiş, geliştirilmiş ve başarıyla test edilmiştir. Elde edilen sonuçlar, önerilen modelin mahremiyet korumalı yörünge verisi yayınlamada sadece araştırmalar için değil aynı zamanda gerçek uygulamalar için de başarıyla kullanılabileceğini göstermektedir.    

Keywords

konum verisi, yörünge verisi, anonimleştirme, diferansiyel mahremiyet, veri yayımlama

Supporting Institution

TÜBİTAK

Project Number

31911873

Thanks

TÜBİTAK tarafından desteklenen 31911873 numaralı proje kapsamında yapılan bu çalışmada yazarlar; başta sağladığı destekler için TÜBİTAK’a, Başarsoft Bilgi Teknolojileri A.Ş.’ye ve teknolojik altyapı sunan Gazi BIDISEC’e teşekkür ederler.

A New Privacy-Aware Model Proposal and Application on Trajectory Data Publishing

Abstract

Location-based services (LBS) make daily life easier with the information and directions they provide. Trajectory data is generated when the location information acquired from users utilizing LBS is sorted according to time. Such kind of data are collected, stored, processed and published by LBS providers for various reasons. Since trajectory data is considered as personal data, attackers may obtain sensitive information about their victims and perform disclosure attacks if the trajectory data is published in original form. There is always a need for up-to-date privacy preserving approaches to address this problem. In this study, in order to publish privacy preserved trajectory data, a new anonymization model based on differential privacy was proposed, developed and successfully tested. The obtained results have shown that the proposed model might be successfully used for privacy preserving trajectory data publishing, not only research purposes but also real time applications.

Keywords

location data, trajectory data, anonymization, differential privacy, data publishing

Project Number

31911873

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