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Davut Deniz Yavuz

Publications and source records attributed to Davut Deniz Yavuz.

2 recordsLinked to original sources

TwinCloud: Secure Cloud Sharing Without Explicit Key Management

With the advent of cloud technologies, there is a growing number of easy-to-use services to store files and share them with other cloud users. By providing security features, cloud service providers try to encourage users to store personal files or corporate documents on their servers. However, their server-side encryption solutions are not satisfactory when the server itself is not trusted. Although, there are several client-side solutions to provide security for cloud sharing, they are not used extensively because of usability issues in key management. In this paper, we propose TwinCloud which is an innovative solution with the goal of providing a secure system to users without compromising the usability of cloud sharing. TwinCloud achieves this by bringing a novel solution to the complex key exchange problem and by providing a simple and practical approach to store and share files by hiding all the cryptographic and key-distribution operations from users. Serving as a gateway, TwinCloud uses two or more cloud providers to store the encryption keys and encrypted files in separate clouds which ease the secure sharing without a need for trust to either of the cloud service providers with the assumption that they do not collude with each other. We implemented TwinCloud as a lightweight application and make it available as open-source. The results of our usability study show the prospect of the secure sharing solution of TwinCloud.

cs.CR↗

Implicit Location Sharing Detection in Social Media from Short Turkish Text

Social media have become a significant venue for information sharing of live updates. Users of social media are producing and sharing large amount of personal data as a part of the live updates. A significant percentage of this data contains location information that can be used by other people for many purposes. Some of the social media users deliberately share their own location information with other social network users. However, a large number of social media users blindly or implicitly share their location without noticing it or its possible consequences. Implicit location sharing is investigated in the current paper. We perform a large scale study on implicit location sharing for one of the most popular social media platform, namely Twitter. After a careful study, we built a dataset of Turkish tweets and manually tagged them. Using machine learning techniques we built classifiers that are able to classify whether a given tweet contains implicit location sharing or not. The classifiers are shown to be very accurate and efficient. Moreover, the best classifier is employed as a browser add-on tool which warns the user whenever an implicit location sharing is predicted from to be released tweet. The paper provides the methodology and the technical analysis as well. Furthermore, it discusses how these techniques can be extended to different social network services and also to different languages.

cs.CY↗