Yidong Cui

A security assessment method for Android applications based on permission model

Permission-based security model of Android restricts applications to access specific resources, but malicious applications can invade more easily in such user-centric pattern. Through the analysis of the Android Permission-based security model and the permission features of Android applications, we establish the permission model to quantify the functional characteristics of the application, and then provide an assessment method in which we use the network visualization techniques and clustering algorithm to determine whether the testing application is potentially malicious application or not so as to help users choose applications before installation. We test the assessment method on 873 applications available online and do the statistic and analysis of the results to find that this method can do efforts in finding potentially malicious applications.

A hybrid social search model based on the user's online social networks

Online social networks (OSNs) have become a new networking platform for connecting people through a variety of mutual relationships. Owing to its widespread use of applications such as email, online shopping, online business networking and instant messaging, OSNs play an indispensable part in peoples work and life. People tend to seek information through their OSNs rather than the traditional search engine. This paper presents a hybrid social search model, which harnesses the users social relation to generate the satisfying results. Upon receiving a users query, the social search model aims to return a list of ranked answerers who might give the correct answers to that query. Two novel algorithms are proposed to calculate the result ranking: 1) Topic Relevance Rank (TRR) evaluates users professional score on the relevant topics; 2) Social Relation Rank (SRR) captures the social relation strength between users. In addition, a topic classification label is defined to control the weight of these two algorithms. We describe the architecture of the social search model and give details of the ranking algorithms. A use-case is described to illustrate the potential applications of the proposed model. 3G RenRen Network, mobile edition of the biggest student social networking site in China is studied and the experiment results show that the two ranking algorithms under control of topic classification label can benefit the social search model evidently.

A new algorithm for multi-mode recommendations in social tagging systems

Social tagging is one of the most important characteristics of web 2.0 services. Different from traditional recommendation algorithms, in social tagging systems, recommendation algorithms involve the ternary relations between users, items and tags. And algorithms that support integrated multi-mode recommendations are very appealing. We propose a multi-mode recommendation algorithm based on higher-order singular value decomposition, and our algorithm handles not only the existing triplets {user, item, tag}, but also the pairs {user, item} with no tags in social tagging system. Meanwhile. We propose a measure for user recommendations. We empirically show that our algorithm outperforms a state-of-the-art algorithm for multi-mode recommendations with a Last.fm dataset.