Yazar "İnan, Ali" seçeneğine göre listele

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    Incorporating Differential Privacy Protection to a Basic Recommendation Engine
    (2020) İnan, Ali
    Recommendation engines analyze ratings data to suggest individuals new products or services based on their past experiences. However, the set of items that an individual has rated and the ratings on these items are critical for protecting individual privacy. Existing work on the problem focus on overly complicated recommendation engines. In this study, we concentrate on the case of a very simple engine protected with a very strong mechanism. Towards this goal, we incorporate differential privacy to an item-based neighborhood predictor. Empirical analyses over large-scale, real-world rating data indicate the efficiency of our proposed solution. Even at very high levels of protection, the rate of loss in prediction accuracy is below 5%, a reasonable trade-off for privacy protection.
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    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.
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    Think before you share in OSNs: Textual content and connection weight put you at higher privacy risk
    (2022) Çoban, Önder; İnan, Ali; Özel, Selma Ayşe
    The widespread use of OSNs has brought forward the issue of privacy protection over OSNs, as sensitive information of users needs to remain private. Most users are unaware of possible privacy risks associated with sharing personal information in their accounts. Privacy settings of OSNs focus on protecting users' information just by providing them with means of configuring the audience of shared information. As such, privacy risk estimation (or scoring) is a hot topic in the field of OSN research and aims to develop risk measuring tools to ensure user privacy in OSNs. Conventional studies in the area often rely on synthetically generated or survey-based data and do not make any effort to infer private attribute values of users to utilize inference success in privacy scoring of these users. In this study, we propose a novel framework that involves populating a response matrix by using attribute inference and obtaining network aware-risk scores not just by using users' connections but weights of these connections as well. We perform attribute inference of users based on both their textual contents and connections. Our rule-based inference mechanism employed on contents produces inference accuracies ranging from 0.54 to 1.0 depending on the attribute at hand. On the other hand, the inference mechanism involving users' social connections produces inference accuracies of 1.0 almost for all of the considered attributes. We present results and challenges of attribute inference and use inferred attributes in privacy risk scoring. In addition, unlike existing works, we use and show that social tie strengths have to be taken into account in network-aware privacy risk scoring.
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    Towards the Design and Implementation of an OSN Crawler: A Case of Turkish Facebook Users
    (2020) Çoban, Önder; İnan, Ali; Özel, Selma Ayşe
    Online Social Networks (OSNs) are extremely popular services that allow users to interact with each other and sharecontent. Due to the large amounts of data shared by users, OSNs are also rich data sources for research in social networkanalysis. Studying the usage of OSNs helps to understand users’ content-sharing behavior and privacy concerns. In order to doso, collecting data is a necessary first step. However, Application Programming Interfaces (APIs) provided by OSN providers haveseveral limitations which make it difficult to access secured information. In this paper, we present the design and implementationof an OSN crawler, discuss the challenges of this task and our workarounds towards accessing public OSN data. Moreover, weperform analyses of the collected data to indicate users’ sharing behavior and give a detailed discussion of these analyses from theperspective of individual privacy protection over OSNs. Our crawler overcomes most of the restrictions of OSN APIs and collectsall forms of OSN user interactions as well as every bit of public data posted on an OSN. Most of the existing studies collectOSN data using focused crawlers and therefore are capable of collecting only the desired type of data. Our crawler, on the otherhand, provides a holistic view. On the popular Facebook OSN, our crawler captures user relationships like kinship, friendship andattributes like profile items, events, posts, comments, replies, meta-data of activities (i.e., posting time, location, tagged users etc.).To the best of our knowledge, ours is the most comprehensive OSN data collection effort and also the first study focused on thebehavior of OSN users in Turkey.
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    Your Username Can Give You Away: Matching Turkish OSN Users with Usernames
    (2021) Çoban, Önder; İnan, Ali; Özel, Selma Ayşe
    Abstract—User profile matching (i.e., user cross-referencing, user identification) aims to find accounts that belong to the same users over different websites or online social networks (OSNs). Solving this problem can be useful for many operations and functionalities such as friend recommendation and link prediction across different OSNs. Additionally, identifying users across different OSNs may enable an adversary to aggregate incomplete information of users. Hereby, an adversary can extract and use online footprint of users to violate their privacy and security via putting them into threats such as identity theft, online stalking, and blackmailing among many others. Usernames are indispensable elements of all websites that require user registration. Even though usernames are generally short strings, they potentially reflect users’ characteristics and habits such as the political sense of belonging, hometown, and so on. In this study, we make an effort to match users of distinct OSNs relying only on their usernames. We use two different approaches based on machine learning and vector-based username similarity to build our learning function. We also explore different feature spaces from the literature and further investigate which approach produces better results. We conducted our experiments on a real-world username data set that is extracted from the OSN accounts of Turkish users we crawled in our previous work. Our results show that building learning function by binary classification outperforms the similarity approach and it achieves the best F-score of 0.921 without feature selection and extension.