Cloud computing is deemed the next-generation information technology(IT) platform, in which a data center is crucial for providing a large amount of computing and storage resources for various service applications with high quality guaranteed. However, cloud users no longer possess their data in a local data storage infrastructure,which would result in auditing for the integrity of outsourced data being a challenging problem, especially for users with constrained computing resources. Therefore, how to help the users complete the verification of the integrity of the outsourced data has become a key issue. Public verification is a critical technique to solve this problem, from which the users can resort to a third-party auditor(TPA) to check the integrity of outsourced data. Moreover,an identity-based(ID-based) public key cryptosystem would be an efficient key management scheme for certificatebased public key setting. In this paper, we combine ID-based aggregate signature and public verification to construct the protocol of provable data integrity. With the proposed mechanism, the TPA not only verifies the integrity of outsourced data on behalf of cloud users, but also alleviates the burden of checking tasks with the help of users' identity. Compared to previous research, the proposed scheme greatly reduces the time of auditing a single task on the TPA side. Security analysis and performance evaluation results show the high efficiency and security of the proposed scheme.
In recent years,with the rapid growth of social network services(SNS),social networks pervade nearly every aspect of our daily lives.Social networks are influencing today’s societal and cultural issues,and changing the way of people seeing themselves.To fully understand the running mechanisms of social networks,in this paper,we aim at series of high knitted and important elements of online social networks.We mainly focus on 3 important but also open research problems,they are(1)structural properties and evolving laws,(2)social crowds and their interaction behaviors and(3)information and its diffusion.In this paper,we review the related work on the 3 problems.Then,we briefly introduce some interesting research directions and our progress on these research problems.
Message forwarding (e.g.,retweeting on Twitter.com) is one of the most popular functions in many existing microblogs,and a large number of users participate in the propagation of information,for any given messages.While this large number can generate notable diversity and not all users have the same ability to diffuse the messages,this also makes it challenging to find the true users with higher spreadability,those generally rated as interesting and authoritative to diffuse the messages.In this paper,a novel method called SpreadRank is proposed to measure the spreadability of users in microblogs,considering both the time interval of retweets and the location of users in information cascades.Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 10 million tweets,and the results showed that our method is consistently better than the PageRank method with the network of retweets and the method of retweetNum which measures the spreadability according to the number of retweets.Moreover,we find that a user with more tweets or followers does not always have stronger spreadability in microblogs.
With the development of online social networks,a special group of online users named organized posters(or Internet water army,Internet paid posters in some literatures) have fl ooded the social network communities. They are organized in groups to post with specific purposes and sometimes even confuse or mislead normal users.In this paper,we study the individual and group characteristics of organized posters. A classifier is constructed based on the individual and group characteristics to detect them. Extensive experimental results on three real datasets demonstrate that our method based on individual and group characteristics using SVM model(IGCSVM) is effective in detecting organized posters and better than existing methods. We take a first look at finding the promoters based on the detected organized posters of our IGCSVM method. Our experiments show that it is effective in detecting promoters.
WANG XiangZHANG ZhilinYU XiangJIA YanZHOU BinLI Shasha
