Provision of overcoming the weakness of OAuth 2.0 protocol in online social networking

The Open Authorization Protocol (OAuth 2.0) was introduced to provide secure and efficient method for providing authorization to the third party applications without sharing user’s credentials. Major social internet players like Facebook, Google and Twitter implement their API’s based on this protocol for enhancing the user experience of social sharing and sign-on. However OAuth doesn’t provides the necessary fine-grained access control or any suggestions. We have proposed an enhancement to the OAuth 2.0 authorization which will provide provision of fine grained authorization suggestions to the users while granting permission to the third party applications in online social networking. Our multi criteria suggestion based model method will utilizes user-based, application based, category-based combination filtering systems. Our category-based combination filtering system is based on decision made by the previous users and the application based permission requests for enhancing the user’s privacy control. We have provided a provision for strengthening the OAuth 2.0 protocol in online social networking websites by proposing OAuth 2.0 extension as a browser based extension which allows various users to compose their privacy settings at the time of installing third party applications. DOI: 10.17762/ijritcc2321-8169.150316

When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference

Risks of Friendships on Social Networks

In this paper, we explore the risks of friends in social networks caused by their friendship patterns, by using real life social network data and starting from a previously defined risk model. Particularly, we observe that risks of friendships can be mined by analyzing users' attitude towards friends of friends. This allows us to give new insights into friendship and risk dynamics on social networks.Comment: 10 pages, 8 figures, 3 tables. To Appear in the 2012 IEEE International Conference on Data Mining (ICDM

A Cross-Disciplined Approach to Exploring the Privacy Paradox: Explaining Disclosure Behaviour Using the Theory of Planned Behaviour

The rise of the Internet and, specifically, the use of social network systems have seen privacy come to the forefront of concern for end-users. One way in which this privacy problem has manifested itself is the privacy paradox; where users concerns do not match their actual behaviour. Research has pointed to a lack of awareness and comprehension as a cause of this paradox and others like it and have recommended improving awareness as a potential solution. However, without knowing the direct, observable causes of the paradox formulating a solution which improves awareness is difficult as it will be based on assumption. As such, this paper proposes the Theory of Planned Behaviour as a sufficiently falsifiable theory to provide a means of exploring the causes of observed paradoxes and as a guide to implementing solutions. The TPB identifies the factors which inform behavioural action as well as a control factor that informs the intention (how easy it is perceived to perform the behaviour) and effects the actual outcome (is it actually easy?). Through an examination of users it is possible to identify which factor is lacking when the paradox is observed; in short the paradox can be robustly explained. Furthermore, this paper proposes that a framework for user interface design can efficiently be modelled around the TPB in order to produce Information Systems which facilitate the desired behaviour of the user in question effectively improving awareness and working towards a solution to observed paradoxes