Biblio

Recommender systems have become ubiquitous in online applications where companies personalize the user experience based on explicit or inferred user preferences. Most modern recommender systems concentrate on finding relevant items for each individual user. In this paper, we describe the problem of directed edge recommendations where the system recommends the best item that a user can gift, share or recommend to another user that he/she is connected to. We propose algorithms that utilize the preferences of both the sender and the recipient by integrating individual user preference models (e.g., based on items each user purchased for themselves) with models of sharing preferences (e.g., gift purchases for others) into the recommendation process. We compare our work to group recommender systems and social network edge labeling, showing that incorporating the task context leads to more accurate recommendations.

Recommender system is to suggest items that might be interest of the users in social networks. Collaborative filtering is an approach that works based on similarity and recommends items liked by other similar users. Trust model adopts users' trust network in place of similarity. Multi-faceted trust model considers multiple and heterogeneous trust relationship among the users and recommend items based on rating exist in the network of trustees of a specific facet. This paper applies genetic algorithm to estimate parameters of multi-faceted trust model, in which the trust weights are calculated based on the ratings and the trust network for each facet, separately. The model was built on Epinions data set that includes consumers' opinion, rating for items and the web of trust network. It was used to predict users' rating for items in different facets and root mean squared of prediction error (RMSE) was considered as a measure of performance. Empirical evaluations demonstrated that multi-facet models improve performance of the recommender system.

Information shared on Twitter is ever increasing and users-recipients are overwhelmed by the number of tweets they receive, many of which of no interest. Filters that estimate the interest of each incoming post can alleviate this problem, for example by allowing users to sort incoming posts by predicted interest (e.g., "top stories" vs. "most recent" in Facebook). Global and personal filters have been used to detect interesting posts in social networks. Global filters are trained on large collections of posts and reactions to posts (e.g., retweets), aiming to predict how interesting a post is for a broad audience. In contrast, personal filters are trained on posts received by a particular user and the reactions of the particular user. Personal filters can provide recommendations tailored to a particular user's interests, which may not coincide with the interests of the majority of users that global filters are trained to predict. On the other hand, global filters are typically trained on much larger datasets compared to personal filters. Hence, global filters may work better in practice, especially with new users, for which personal filters may have very few training instances ("cold start" problem). Following Uysal and Croft, we devised a hybrid approach that combines the strengths of both global and personal filters. As in global filters, we train a single system on a large, multi-user collection of tweets. Each tweet, however, is represented as a feature vector with a number of user-specific features.

Peer-To-Peer (P2P) networks open up great possibilities for intercommunication, collaborative and social projects like file sharing, communication protocols or social networks while offering advantages over the conventional Client-Server model of computing pattern. Such networks counter the problems of centralized servers such as that P2P networks can scale to millions without additional costs. In previous work, we presented Distributed Data Structure (DDS) which offers a middle-ware scheme for distributed applications. This scheme builds on top of DHT (Distributed Hash Table) based P2P overlays, and offers distributed data storage services as a middle-ware it still needs to address security issues. The main objective of this paper is to investigate possible ways to handle the security problem for DDS, and to develop a possibly reusable security architecture for access control for secure distributed data structures in P2P networks without depending on trusted third parties.

Trust in Social Internet of Things has allowed to open new horizons in collaborative networking, particularly by allowing objects to communicate with their service providers, based on their relationships analogy to human world. However, strengthening trust is a challenging task as it involves identifying several influential factors in each domain of social-cyber-physical systems in order to build a reliable system. In this paper, we address the issue of understanding and evaluating honesty that is an important trust metric in trustworthiness evaluation process in social networks. First, we identify and define several trust attributes, which affect directly to the honesty. Then, a subjective computational model is derived based on experiences of objects and opinions from friendly objects with respect to identified attributes. Based on the outputs of this model a final honest level is predicted using regression analysis. Finally, the effectiveness of our model is tested using simulations.

The ability to discover patterns of interest in criminal networks can support and ease the investigation tasks by security and law enforcement agencies. By considering criminal networks as a special case of social networks, we can properly reuse most of the state-of-the-art techniques to discover patterns of interests, i.e., hidden and potential links. Nevertheless, in time-sensible scenarios, like the one involving criminal actions, the ability to discover patterns in a (near) real-time manner can be of primary importance.In this paper, we investigate the identification of patterns for link detection and prediction on an evolving criminal network. To extract valuable information as soon as data is generated, we exploit a stream processing approach. To this end, we also propose three new similarity social network metrics, specifically tailored for criminal link detection and prediction. Then, we develop a flexible data stream processing application relying on the Apache Flink framework; this solution allows us to deploy and evaluate the newly proposed metrics as well as the ones existing in literature. The experimental results show that the new metrics we propose can reach up to 83% accuracy in detection and 82% accuracy in prediction, resulting competitive with the state of the art metrics.

Software has emerged as a significant part of many domains, including financial service platforms, social networks and vehicle control. Standards organizations have responded to this by creating regulations to address issues such as safety and privacy. In this context, compliance of software with standards has emerged as a key issue. For software development organizations, compliance is a complex and costly goal to achieve and is often accomplished by producing so-called assurance cases, which demonstrate that the system indeed satisfies the property imposed by a standard (e.g., safety, privacy, security). As systems and standards undergo evolution for a variety of reasons, maintaining assurance cases multiplies the effort. In this work, we propose to exploit the connection between the field of model management and the problem of compliance management and propose methods that use model management techniques to address compliance scenarios such as assurance case evolution and reuse. For validation, we ground our approaches on the automotive domain and the ISO 26262 standard for functional safety of road vehicles.

Crowd management in urban settings has mostly relied on either classical, non-automated mechanisms or spontaneous notifications/alerts through social networks. Such management techniques are heavily marred by lack of comprehensive control, especially in terms of averting risks in a manner that ensures crowd safety and enables prompt emergency response. In this paper, we propose a Markov Decision Process Scheme MDP to realize a smart infrastructure that is directly aimed at crowd management. A key emphasis of the scheme is a robust and reliable scalability that provides sufficient flexibility to manage a mixed crowd (i.e., pedestrian, cyclers, manned vehicles and unmanned vehicles). The infrastructure also spans various population settings (e.g., roads, buildings, game arenas, etc.). To realize a reliable and scalable crowd management scheme, the classical MDP is decomposed into Local MDPs with smaller action-state spaces. Preliminarily results show that the MDP decomposition can reduce the system global cost and facilitate fast convergence to local near-optimal solution for each L-MDP.

Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems: in some cases, a big graph is chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to certain suboptimality from the interaction among different pieces. In other cases, links between different parts may show up in the running time and/or network communications cost, hence the desire to have small cut size. We study a distributed balanced partitioning problem where the goal is to partition the vertices of a given graph into k pieces, minimizing the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks, e.g., MapReduce. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, e.g., via a hierarchical clustering or Hilbert curves. Then we apply four different techniques such as local swaps, minimum cuts on partition boundaries, as well as contraction and dynamic programming. Our empirical study compares the above techniques with each other, and to previous work in distributed algorithms, e.g., a label propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: we notice, e.g., 15-25% reduction in cut size over [UB13]. We also observe that our algorithms allow for scalable distributed implementation for any number of partitions. Finally, we apply our techniques for the Google Maps Driving Directions to minimize the number of multi-shard queries with the goal of saving in CPU usage. During live experiments, we observe an ≈ 40% drop in the number of multi-shard queries when comparing our method with a standard geography-based method.

Graph reordering is a powerful technique to increase the locality of the representations of graphs, which can be helpful in several applications. We study how the technique can be used to improve compression of graphs and inverted indexes. We extend the recent theoretical model of Chierichetti et al. (KDD 2009) for graph compression, and show how it can be employed for compression-friendly reordering of social networks and web graphs and for assigning document identifiers in inverted indexes. We design and implement a novel theoretically sound reordering algorithm that is based on recursive graph bisection. Our experiments show a significant improvement of the compression rate of graph and indexes over existing heuristics. The new method is relatively simple and allows efficient parallel and distributed implementations, which is demonstrated on graphs with billions of vertices and hundreds of billions of edges.

When people utilize social applications and services, their privacy suffers a potential serious threat. In this article, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement, which takes account of local and global structural characteristics of data, information obtained from auxiliary data, and knowledge inherited from ongoing de-anonymization results. By analyzing the measurement on real datasets, we find that some data can potentially be de-anonymized accurately and the other can be de-anonymized in a coarse granularity. Utilizing this property, we present a US-based De-Anonymization (DA) framework, which iteratively de-anonymizes data with accuracy guarantee. Then, to de-anonymize large-scale data without knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. By smartly working on two core matching subgraphs, ADA achieves high de-anonymization accuracy and reduces computational overhead. Finally, we examine the presented de-anonymization attack on three well-known mobility traces: St Andrews, Infocom06, and Smallblue, and three social datasets: ArnetMiner, Google+, and Facebook. The experimental results demonstrate that the presented de-anonymization framework is very effective and robust to noise. The source code and employed datasets are now publicly available at SecGraph [2015].

In order to identify a personalized story, suitable for the needs of large masses of visitors and tourists, our work has been aimed at the definition of appropriate models and solutions of fruition that make the visit experience more appealing and immersive. This paper proposes the characteristic functionalities of narratology and of the techniques of storytelling for the dynamic creation of experiential stories on a sematic basis. Therefore, it represents a report about sceneries, implementation models and architectural and functional specifications of storytelling for the dynamic creation of functional contents for the visit. Our purpose is to indicate an approach for the realization of a dynamic storytelling engine that can allow the dynamic supply of narrative contents, not necessarily predetermined and pertinent to the needs and the dynamic behaviors of the users. In particular, we have chosen to employ an adaptive, social and mobile approach, using an ontological model in order to realize a dynamic digital storytelling system, able to collect and elaborate social information and contents about the users giving them a personalized story on the basis of the place they are visiting. A case of study and some experimental results are presented and discussed.

In the era of Cloud and Social Networks, mobile devices exhibit much more powerful abilities for big media data storage and sharing. However, many users are still reluctant to share/store their data via clouds due to the potential leakage of confidential or private information. Although some cloud services provide storage encryption and access protection, privacy risks are still high since the protection is not always adequately conducted from end-to-end. Most customers are aware of the danger of letting data control out of their hands, e.g., Storing them to YouTube, Flickr, Facebook, Google+. Because of substantial practical and business needs, existing cloud services are restricted to the desired formats, e.g., Video and photo, without allowing arbitrary encrypted data. In this paper, we propose a format-compliant end-to-end privacy-preserving scheme for media sharing/storage issues with considerations for big data, clouds, and mobility. To realize efficient encryption for big media data, we jointly achieve format-compliant, compression-independent and correlation-preserving via multi-channel chained solutions under the guideline of Markov cipher. The encryption and decryption process is integrated into an image/video filter via GPU Shader for display-to-display full encryption. The proposed scheme makes big media data sharing/storage safer and easier in the clouds.

The integration of social networking concepts into the Internet of things has led to the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability in information/service discovery. Within this scenario, we focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. In the subjective model each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the friends in common with the potential service providers. In the objective model, the information about each node is distributed and stored making use of a distributed hash table structure so that any node can make use of the same information. Simulations show how the proposed models can effectively isolate almost any malicious nodes in the network at the expenses of an increase in the network traffic for feedback exchange.

The dynamic nature of the Web 2.0 and the heavy obfuscation of web-based attacks complicate the job of the traditional protection systems such as Firewalls, Anti-virus solutions, and IDS systems. It has been witnessed that using ready-made toolkits, cyber-criminals can launch sophisticated attacks such as cross-site scripting (XSS), cross-site request forgery (CSRF) and botnets to name a few. In recent years, cyber-criminals have targeted legitimate websites and social networks to inject malicious scripts that compromise the security of the visitors of such websites. This involves performing actions using the victim browser without his/her permission. This poses the need to develop effective mechanisms for protecting against Web 2.0 attacks that mainly target the end-user. In this paper, we address the above challenges from information flow control perspective by developing a framework that restricts the flow of information on the client-side to legitimate channels. The proposed model tracks sensitive information flow and prevents information leakage from happening. The proposed model when applied to the context of client-side web-based attacks is expected to provide a more secure browsing environment for the end-user.

The dynamic nature of the Web 2.0 and the heavy obfuscation of web-based attacks complicate the job of the traditional protection systems such as Firewalls, Anti-virus solutions, and IDS systems. It has been witnessed that using ready-made toolkits, cyber-criminals can launch sophisticated attacks such as cross-site scripting (XSS), cross-site request forgery (CSRF) and botnets to name a few. In recent years, cyber-criminals have targeted legitimate websites and social networks to inject malicious scripts that compromise the security of the visitors of such websites. This involves performing actions using the victim browser without his/her permission. This poses the need to develop effective mechanisms for protecting against Web 2.0 attacks that mainly target the end-user. In this paper, we address the above challenges from information flow control perspective by developing a framework that restricts the flow of information on the client-side to legitimate channels. The proposed model tracks sensitive information flow and prevents information leakage from happening. The proposed model when applied to the context of client-side web-based attacks is expected to provide a more secure browsing environment for the end-user.

The Communities vary from country to country. There are civil societies and rural communities, which also differ in terms of geography climate and economy. This shows that the use of social networks vary from region to region depending on the demographics of the communities. So, in this paper, we researched the most important problems of the Social Network, as well as the risk which is based on the human elements. We raised the problems of social networks in the transformation of societies to another affected by the global economy. The social networking integration needs to strengthen social ties that lead to the existence of these problems. For this we focused on the Internet security risks over the social networks. And study on Risk Management, and then look at resolving various problems that occur from the use of social networks.
 

The dynamic nature of the Web 2.0 and the heavy obfuscation of web-based attacks complicate the job of the traditional protection systems such as Firewalls, Anti-virus solutions, and IDS systems. It has been witnessed that using ready-made toolkits, cyber-criminals can launch sophisticated attacks such as cross-site scripting (XSS), cross-site request forgery (CSRF) and botnets to name a few. In recent years, cyber-criminals have targeted legitimate websites and social networks to inject malicious scripts that compromise the security of the visitors of such websites. This involves performing actions using the victim browser without his/her permission. This poses the need to develop effective mechanisms for protecting against Web 2.0 attacks that mainly target the end-user. In this paper, we address the above challenges from information flow control perspective by developing a framework that restricts the flow of information on the client-side to legitimate channels. The proposed model tracks sensitive information flow and prevents information leakage from happening. The proposed model when applied to the context of client-side web-based attacks is expected to provide a more secure browsing environment for the end-user.

The objective of the paper is to propose a social network security management model for a multi-tenancy SaaS application using Unified Communications as a Service (UCaaS) approach. The earlier security management models do not cover the issues when data inadvertently get exposed to other users due to poor implementation of the access management processes. When a single virtual machine moves or dissolves in the network, many separate machines may bypass the security conditions that had been implemented for its neighbors which lead to vulnerability of the hosted services. When the services are multi-tenant, the issue becomes very critical due to lack of asynchronous asymmetric communications between virtual when more number of applications and users are added into the network creating big data issues and its identity. The TRAIN model for the security management using PC-FAST algorithm is proposed in order to detect and identify the communication errors between the hosted services.
 

With the proliferation of WiFi-enabled devices, people expect to be able to use them everywhere, be it at work, while commuting, or when visiting friends. In the latter case, home owners are confronted with the burden of controlling the access to their WiFi router, and usually resort to simply sharing the password. Although convenient, this solution breaches basic security principles, and puts the burden on the friends who have to enter the password in each and every of their devices. The use of social networks, specifying the trust relations between people and devices, provides for a more secure and more friendly authentication mechanism. In this paper, we progress the state-of-the-art by abandoning the centralized solution to embed social networks in WiFi authentication; we introduce EAP-SocTLS, a decentralized approach for authentication and authorization of WiFi access points and other devices, exploiting the embedded trust relations. In particular, we address the (quadratic) search complexity when indirect trust relations, like the smartphone of a friend's kid, are involved. We show that the simple heuristic of limiting the search to friends and devices in physical proximity makes for a scalable solution. Our prototype implementation, which is based on WebID and EAP-TLS, uses WiFi probe requests to determine the pool of neighboring devices and was shown to reduce the search time from 1 minute for the naive policy down to 11 seconds in the case of granting access over an indirect friend.