Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com.     

Incremental mining of the schema of semistructured data

Semistructued data are specified in lack of any fixed and rigid schema,even though typically some implicit structure appears in the data.The huge amounts of on-line applications make it important and imperative to mine the schema of semistructured data ,both for the users(e.g.,to gather useful information and facilitate querying)and for the systems (e.g.,to optimize access).The critical problem is to discover the hidden structure in the semistructured data.Current methods in extracting Web data structure are either in a general way independent of application background,or bound in some concrete environment such as HTML,XML etc.But both face the burden of expensive cost and difficulty in keeping along with the frequent and complicated variances of Web data.In this paper,the problem of incremental mining of schema for semistructured data after the update of the raw data is discusses.An algorithm for incrementally mining the schema of semistructured data is provided,and some experimental results are also given,which show that incremental mining for semistructured data is more efficient than non-incremental mining.     

Dynamic data auditing scheme for big data storage

When users store data in big data platforms,the integrity of outsourced data is a major concern for data owners due to the lack of direct control over the data.However,the existing remote data auditing schemes for big data platforms are only applicable to static data.In order to verify the integrity of dynamic data in a Hadoop big data platform,we presents a dynamic auditing scheme meeting the special requirement of Hadoop.Concretely,a new data structure,namely Data Block Index Table,is designed to support dynamic data operations on HDFS(Hadoop distributed file system),including appending,inserting,deleting,and modifying.Then combined with the MapReduce framework,a dynamic auditing algorithm is designed to audit the data on HDFS concurrently.Analysis shows that the proposed scheme is secure enough to resist forge attack,replace attack and replay attack on big data platform.It is also efficient in both computation and communication.     

Local Community Detection Using Link Similarity

Exploring local community structure is an appealing problem that has drawn much recent attention in the area of social network analysis.As the complete information of network is often difficult to obtain,such as networks of web pages,research papers and Facebook users,people can only detect community structure from a certain source vertex with limited knowledge of the entire graph.The existing approaches do well in measuring the community quality,but they are largely dependent on source vertex and putting too strict policy in agglomerating new vertices.Moreover,they have predefined parameters which are difficult to obtain.This paper proposes a method to find local community structure by analyzing link similarity between the community and the vertex.Inspired by the fact that elements in the same community are more likely to share common links,we explore community structure heuristically by giving priority to vertices which have a high link similarity with the community.A three-phase process is also used for the sake of improving quality of community structure.Experimental results prove that our method performs effectively not only in computer-generated graphs but also in real-world graphs.     

Topology-Based Recommendation of Users in Micro-Blogging Communities

Nowadays,more and more users share real-time news and information in micro-blogging communities such as Twitter,Tumblr or Plurk.In these sites,information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows,named followees.With the increasing number of registered users in this kind of sites,finding relevant and reliable sources of information becomes essential.The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation.To address this problem,we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources.Experimental evaluation conducted with a group of users is reported,demonstrating the potential of the approach.     

NaEPASC: a novel and efficient public auditing scheme for cloud data

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.     

The Method of Data Pre-processing in Grey Information Systems

In this paper, a method of data pre-processing in grey information systems was proposed to deal with grey information. The binning technique was introduced to smooth noisy data used for grey relative analysis. It constructed the function of grey relative coefficient for each null value and filled up the null value with the solution of the function. It also can be used to detect noisy data. This method is an application of Grey System Theory in data pre-processing. It has great significance in filling up null values and detecting noisy data in the “poor”information database.     

Dynamic fire 3D modeling using a real-time stereovision system

This work presents a new framework for three-dimensional modeling of dynamic fires present in unstructured scenes. The proposed approach addresses the problem of segmenting fire regions using information from YUV and RGB color spaces. Clustering is also used to extract salient points from a pair of stereo images. These points are then used to reconstruct 3D positions in the scene. A matching strategy is proposed to deal with mismatches due to occlusions and missing data. The obtained data are fitted in a 3D ellipsoid in order to model the enclosing fire volume. This form is then used to compute dynamic fire characteristics like its position, dimension, orientation, heading direction, etc. These results are of great importance for fire behavior monitoring and prediction.     

Transfer active learning by querying committee简

In real applications of inductive learning for classifi cation, labeled instances are often defi cient, and labeling them by an oracle is often expensive and time-consuming. Active learning on a single task aims to select only informative unlabeled instances for querying to improve the classifi cation accuracy while decreasing the querying cost. However, an inevitable problem in active learning is that the informative measures for selecting queries are commonly based on the initial hypotheses sampled from only a few labeled instances. In such a circumstance, the initial hypotheses are not reliable and may deviate from the true distribution underlying the target task. Consequently, the informative measures will possibly select irrelevant instances. A promising way to compensate this problem is to borrow useful knowledge from other sources with abundant labeled information, which is called transfer learning. However, a signifi cant challenge in transfer learning is how to measure the similarity between the source and the target tasks. One needs to be aware of different distributions or label assignments from unrelated source tasks;otherwise, they will lead to degenerated performance while transferring. Also, how to design an effective strategy to avoid selecting irrelevant samples to query is still an open question. To tackle these issues, we propose a hybrid algorithm for active learning with the help of transfer learning by adopting a divergence measure to alleviate the negative transfer caused by distribution differences. To avoid querying irrelevant instances, we also present an adaptive strategy which could eliminate unnecessary instances in the input space and models in the model space. Extensive experiments on both the synthetic and the real data sets show that the proposed algorithm is able to query fewer instances with a higher accuracy and that it converges faster than the state-of-the-art methods.     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

User perceptions of e-quality of and affinity with virtual communities: The effect of individual differences

Following the information systems (IS) success model, this study explores the effect of individual differences on users’ perceptions of virtual communities in terms of e-quality (namely, information quality, system quality and service quality) of and affinity with virtual communities given individual differences are crucial in determining how individuals think and respond to the environment. This study examines the effect of individual differences on virtual community success dimensions from both physical and psychological perspectives, which we think presents a new view for virtual community research and practice alike. Data collected from users of virtual communities were used for data analysis. First, the cluster analysis was applied and five personality trait clusters were identified in terms of extraversion, agreeableness, openness to new experience, conscientiousness and neuroticism. Then, the independent sample t test and one-way analysis of variance (ANOVA) were employed. The effect of individual differences in terms of gender, age, position, experience with virtual communities as well as the five personality trait clusters on users’ perceptions of e-quality of and affinity with virtual communities was explored and discussed.     

Community detection in social networks using user frequent pattern mining

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.     

基于P2P的数据索引与查询

现有的基于DHT的P2P系统只能通过精确匹配整个数据识别器来查询数据。但用户一般只有部分信息可以确认这些信息,为了在用户需求和基于DHT的P2P系统能力间架起一座桥梁,本文提出了一种新的索引和查询数据的方法。这种方法在数据的XML描述上建立了DHT索引,并方便了Xpath表达式的复杂查询。     

Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume

The world health organization (WHO) terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific treatment. Early and accurate detection of spread in affected regions can save precious lives. Despite the severity of the disease, a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text streams. However, the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data, as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are required. This research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache Hadoop. The method consists of two main parts: the first part collects data from social media using Apache Flume, while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache Hive. To overcome the noisy and unstructured nature of the data, the process of extracting information is characterized by pre and post-filtration phases. As a result, only with the integration of Flume and Hive with filtration and polarity analysis, can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social network. We introduce how the Apache Hadoop ecosystem – Flume and Hive – can provide a sentiment analysis capability by storing and processing large amounts of data. An important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines.     

Privacy protection of online social network users,against attribute inference attacks,through the use of a set of exhaustive rules

Neural Computing and Applications - A malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By...     

DiDuSoNet: A P2P architecture for distributed Dunbar-based social networks

Online Social Networks (OSNs) are becoming more and more popular on the Web. Distributed Online Social Networks (DOSNs) are OSNs which do not exploit a central server for storing users data and enable users to have more control on their profile content, ensuring a higher level of privacy. In a DOSN there are some technical challenges to face. One of the most important challenges is the data availability problem when a user is offline. In this paper we propose DiDuSoNet, a novel P2P Distributed Online Social Network where users can exercise full access control on their data. Our system exploits trust relationships for providing a set of important social services, such as trustness, information diffusion, and data availability. In this paper we show how our system manages the problem of data availability by proposing a new P2P dynamic trusted storage approach. By following the Dunbar concept, our system stores the data of a user only on a restricted number of friends which have regular contacts with him/her. Differently from other approaches, nodes chosen to keep data replicas are not statically defined but dynamically change according to users churn. In according to our previous work, we use only two online profile replicas at time. By using real Facebook data traces we prove that our approach offers high availability.     

不确定数据库中概率top-k和排序查询算法

传感器网络等技术的广泛应用产生了大量不确定数据。近年来,对于不确定数据的处理和查询成为数据库和数据挖掘领域研究的热点。其中,传统关系数据库中的top-k查询和排序查询怎样拓展到不确定数据是其中的焦点之一。研究近年来提出的不确定数据库上top-k查询和排序查询算法,归纳和比较目前各种不同查询算法所适应的语义世界和应用场景,并详细分析各种算法的执行效率和算法复杂度。另外,对于不确定数据top-k查询和排序查询所面临的挑战和可能的研究方向进行了总结。     

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.     

基于内容的多媒体数据递进目标搜索

为了有效提高移动终端多媒体信息的能力,讨论了一种新的多媒体信息查询方法——基于内容的递进目标搜索,提出了“查询流”的技术,通过多媒体查询方法（MQF）与XML片断请求单元及片断更新单元的结合,使得用户对多媒体信息的查询逐步进行,先查询相关的元数据描述,然后查询最终结果。这样有效降低了查询的数据通信量,非常适合于配置较低、信道有限的移动终端实现多媒体信息的查询。