Private discovery of common social contacts

Digital services that are offered, and consumed, on the basis of social relationships form the backbone of social clouds—an emerging new concept that finds its roots in online social networks. The latter have already taken an essential role in people’s daily life, helping users to build and reflect their social relationships to other participants. A key step in establishing new links entails the reconciliation of shared contacts and friends. However, for many individuals, personal relationships belong to the private sphere, and, as such, should be concealed from potentially prying eyes of strangers. Consequently, the transition toward social clouds cannot set aside mechanisms to control the disclosure of social links. This paper motivates and introduces the concept of Private Discovery of Common Social Contacts, which allows two users to assess their social proximity through interaction and learn the set of contacts (e.g., friends) that are common to both users, while hiding contacts that they do not share. We realize private contact discovery using a new cryptographic primitive, called contact discovery scheme (CDS), whose functionality and privacy is formalized in this work. To this end, we define a novel privacy feature, called contact-hiding, that captures our strong privacy goals. We also propose the concept of contact certification and show that it is essential to thwart impersonation attacks on social relationships. We build provably private and realistically efficient CDS protocols for private discovery of mutual contacts. Our constructions do not rely on a trusted third party (TTP)—all contacts are managed independently by the users. The practicality of our proposals is confirmed both analytically and experimentally on different computing platforms. We show that they can be efficiently deployed on smartphones, thus allowing ad hoc and ubiquitous contact discovery outside of existing social networks. Our CDS constructions allow users to select their (certified) contacts to be included in individual protocol executions. That is, users may perform context-dependent contact discovery using any subset (circle) of their contacts.     

Annotation-based access control for collaborative information spaces

Web 2.0 social platforms (e.g., Flickr, YouTube) and Collaborative Working Environments (e.g., Microsoft SharePoint, BSCW) provide Web-based collaborative information spaces which enable common users and/or professionals to work together and share their online resources. Most of these collaborative information spaces provide role-based or group-based, coarse-grained access control policies which cannot successfully cope with the requirements posed by massive and open collaboration. In this paper, we present an annotation-based access control (AnBAC) model supported by a Collaboration Vocabulary (CoVoc) as a more flexible and user-centric access control approach. Based on this, we developed two tools: Uncle-Share is a gadget that provides annotation-based access control and can be equipped with CoVoc for annotating collaborative relationships. Who-With-Whom uses CoVoc to visualize extended social networks in order to help users to select appropriate contacts to grant access to resources.     

Web-Traveler Policies for Images on Social Networks

Images are one of the most popular type of contents shared on these sites. One of the most popular types of contents shared on these sites is image. Most of these networks offer some rudimentary forms of access controls such as allowing the users to choose who can view their profiles or the images uploaded by them. These controls however apply only in the perimeter of the users’ direct control such as desktops, profiles etc. Users have no control over their content once it is downloaded by others. In order to enable a user to truly maintain control over his content, new access control mechanisms must be designed so as to enable users to control their content even when managed by others. Towards fulfilling this gap, in this paper we propose the concept of “web-traveler policies”. Web-traveler policies allow the user to specify who can view, upload or download a given image within a social network. The unique, innovative feature of web-traveler policies is that they travel with the image, as long as it is hosted on the given social networking site. Additionally, we explore the possibility of extending these controls across different sites, by using the concept of mashups. In the paper we prove the feasibility of this approach, by implementing a working prototype of our approach on a real open source social network platform. We test the performance and scalability of such architecture under heavy user loads and also its resilience towards naïve attacks. This work is a first innovative step toward scalable systems for providing each user with a centralized system for his/her own content’s protection all over the web.     

Toward tracing and revoking schemes secure against collusion and any form of secret information leakage

Tracing and revoking schemes enable a center to deliver protected content to a subset of privileged users of a given universe. The main property these schemes enjoy is that traitors, who illegally help unauthorized users to set up a pirate decoder for gaining access to the protected content, can be identified and removed from the privileged subset. Historically, traitors have been modeled as users who privately share their secret information with unauthorized users. However, in the Pirates 2.0 attack model, traitors collaborate in public and partially share their secret information with a certified guarantee of anonymity. Several classes of tracing and revoking schemes, like tree-based tracing and revoking schemes and code-based tracing schemes, are subject to such a new threat. In this paper we propose methods to cope with the Pirates 2.0 attack. We focus our attention on the class of tree-based schemes. We start by discussing some simple techniques, which can partially help to deal with the attack, and point out their limits. Then, looking through the literature, we recover some ideas, which can be used to strengthen tracing and revoking schemes. We also analyze the trade-off which can be obtained by applying these ideas to the schemes. Finally, we describe new hybrid schemes, obtained by mixing previous constructions, which can be used to face up the Pirates 2.0 attack.     

Enforcing transparent access to private content in social networks by means of automatic sanitization

Social networks have become an essential meeting point for millions of individuals willing to publish and consume huge quantities of heterogeneous information. Some studies have shown that the data published in these platforms may contain sensitive personal information and that external entities can gather and exploit this knowledge for their own benefit. Even though some methods to preserve the privacy of social networks users have been proposed, they generally apply rigid access control measures to the protected content and, even worse, they do not enable the users to understand which contents are sensitive. Last but not least, most of them require the collaboration of social network operators or they fail to provide a practical solution capable of working with well-known and already deployed social platforms. In this paper, we propose a new scheme that addresses all these issues. The new system is envisaged as an independent piece of software that does not depend on the social network in use and that can be transparently applied to most existing ones. According to a set of privacy requirements intuitively defined by the users of a social network, the proposed scheme is able to: (i) automatically detect sensitive data in users’ publications; (ii) construct sanitized versions of such data; and (iii) provide privacy-preserving transparent access to sensitive contents by disclosing more or less information to readers according to their credentials toward the owner of the publications. We also study the applicability of the proposed system in general and illustrate its behavior in two case studies.     

基于ESU图的活动社交网络用户参加活动推荐

活动社交网络(EBSNs)为用户提供了方便的组织、参加和分享社交活动的平台。该文面向EBSNs活动推荐问题,提出了包含活动(Event)、主办方(Sponsor)和用户(User)的ESU图模型,深入揭示了EBSNs的实体及其社交关系。因为用户参加活动受多个因素影响,我们提出了基于ESU图的活动推荐多因素决策模型,包括社交影响力、活动内容、活动地点及活动时间。根据ESU图特点,提出了基于双向重启随机游走算法BD-RWR的实体重要度计算方法。选取真实的EBSNs平台—豆瓣同城验证所提方法的有效性。实验结果表明,该文提出的ESU图模型及融合了多因素的活动推荐模型,与已有最新方法相比,有效地提升了用户参加活动的推荐效果。     

The role of social interaction on users motivation to exercise: A persuasive web framework to enhance the self-management of a healthy lifestyle

The current research guidelines of the European community suggest the importance of the development of systems that help users manage their health themselves. The increasing amount of communication technologies and devices from which users can access information, and the possibility to interact through social media channels, play an important role in this scenario. Based on these considerations, in this paper we present an innovative persuasive web application, designed both to exploit social networking sites and to cooperate with a mobile application that already operates in the e-health and motivational domains. In particular, the innovative aspects introduced by the web application are the possibility to access also from a web browser some features previously available only through a mobile application and a more direct and user-friendly integration of social network sites. Indeed, thanks to an extensive interaction with the Facebook social network, users are allowed to share their experience with the application. This generates a strong social influence effect, which inspires and motivates other users to improve their exercising activity. Experimental results put in evidence that our web application, also thanks to social interactions, is favoring an enhancement of users’ motivation to a more active lifestyle. This is mainly due to its capability to have an impact on the other users thanks to the posts generated on the Facebook social network.     

Privacy policies for shared content in social network sites

Social networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of subscribed users. Social networks enable a form of self-expression for users and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social network sites, they do not provide any mechanisms for collective management of privacy settings for shared content. In this paper, using game theory, we model the problem of collective enforcement of privacy policies on shared data. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness and that rewards users who promote co-ownership. Our approach enables social network users to compose friendship based policies based on distances from an agreed upon central user selected using several social networks metrics. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge, this is the first time such a privacy protection mechanism for social networking has been proposed. We also extend our mechanism so as to support collective enforcement across multiple social network sites. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today’s most popular social networks. Through our implementation, we show the feasibility of such approach and show that it can be implemented with a minimal increase in overhead to end-users. We complete our analysis by conducting a user study to investigate users’ understanding of co-ownership, usefulness and understanding of our approach. Users responded favorably to the approach, indicating a general understanding of co-ownership and the auction, and found the approach to be both useful and fair.     

Mechanisms for building autonomically scalable services on cooperatively shared computing platforms

We present here a system architecture and its underlying mechanisms for building autonomically scalable and resilient services on cooperatively shared computing platforms. Specifically, our focus is on utilizing computing platforms exhibiting the following characteristics. The resources at a node in such platforms are allocated to competing users on fair‐share basis, without any reserved resource capacities for any user. There is no platform‐wide resource manager for the placement of users on different nodes. The users independently select nodes for their applications. Moreover, a node can become unavailable at any time due to crashes or shutdowns. Building scalable services in such environments poses unique challenges due to node‐level fluctuations in the available resource capacities and node crashes. The service load may surge in a short time due to flash crowds. Autonomic scaling of service capacity is performed by dynamic control of the degree of service replication based on the estimated service capacity and the observed load. We present here models for estimating the service capacity at a node under fluctuating operating conditions. Furthermore, we develop adaptive and agile load distribution mechanisms for distributing load among replicas based on their time‐varying service capacities. We present the results of our evaluations of these mechanisms on PlanetLab, which exemplifies the platform level characteristics considered here.Copyright © 2013 John Wiley & Sons, Ltd.     

Cooperative content dissemination in multi-channel WLAN hotspots

It is common for hotspots in airports, cafes, and malls to deploy info-stations on their access points to provide local information such as flight schedule, multimedia access, sales and discount deals. The dissemination service, however, is limited by the wireless throughput in hotspots and cannot scale to a large number of users. This paper designs a new architecture that significantly improves the throughput of info-stations. We observe that usually the number of collocated info-stations is smaller than the number of orthogonal channels in IEEE 802.11, leaving some orthogonal channels unused, and thus wasting their capacity. We also observe that many users of an info-station download the same objects. Thus, we advocate an architecture, called hybrid-WLAN (H-WLAN), where mobile nodes can retrieve objects (multimedia content or information) from the info-station in the infrastructure mode, or exploit the idle 802.11 channels to cooperatively share their content with neighboring nodes in an ad-hoc manner. Our H-WLAN contains two components. The first component directs each object request to a nearby ad-hoc node that has the requested object, while avoids overloading any of the nodes. The second component balances the load across channels taking into account the popularity of the disseminated objects. We evaluate our design via simulations. The results show that our H-WLAN improves channel utilization and provides significantly higher throughput.     

Mosco: a privacy-aware middleware for mobile social computing

The proliferation of mobile devices coupled with Internet access is generating a tremendous amount of highly personal and sensitive data. Applications such as location-based services and quantified self harness such data to bring meaningful context to users’ behavior. As social applications are becoming prevalent, there is a trend for users to share their mobile data. The nature of online social networking poses new challenges for controlling access to private data, as compared to traditional enterprise systems. First, the user may have a large number of friends, each associated with a unique access policy. Second, the access control policies must be dynamic and fine-grained, i.e. they are content-based, as opposed to all-or-nothing. In this paper, we investigate the challenges in sharing of mobile data in social applications. We design and evaluate a middleware running on Google App Engine, named Mosco, that manages and facilitates sharing of mobile data in a privacy-preserving manner. We use Mosco to develop a location sharing and a health monitoring application. Mosco helps shorten the development process. Finally, we perform benchmarking experiments with Mosco, the results of which indicate small overhead and high scalability.     

DYSCS: A platform to build geographically and semantically enhanced social content sites

Social content sites allow ordinary internet users to upload, edit, share, and annotate Web content with freely chosen keywords called tags. However, tags are only useful to the extent that they are processable by users and machines, which is often not the case since users frequently provide ambiguous and idiosyncratic tags. Thereby, many social content sites are starting to allow users to enrich their tags with semantic metadata, such as the GeoSocial Content Sites, for example, where users can annotate their tags with geographic metadata. But geographic metadata alone only unveils a very specific facet of a tag, which leads to the need for more general purpose semantic metadata. This paper introduces DYSCS – Do it Yourself Social Content Sites – a platform that combines Web 2.0 and Semantic Web technologies for assisting users in creating their own social content sites enriched with geographic and general purpose semantics. Moreover, DYSCS is highly reusable and interoperable, which are consequences of its ontology driven architecture.     

Finding Diachronic Like‐Minded Users

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.     

Benefits and Drawbacks of Redundant Batch Requests

Most parallel computing platforms are controlled by batch schedulers that place requests for computation in a queue until access to compute nodes is granted. Queue waiting times are notoriously hard to predict, making it difficult for users not only to estimate when their applications may start, but also to pick among multiple batch-scheduled platforms the one that will produce the shortest turnaround time. As a result, an increasing number of users resort to “redundant requests”: several requests are simultaneously submitted to multiple batch schedulers on behalf of a single job; once one of these requests is granted access to compute nodes, the others are canceled. Using simulation as well as experiments with a production batch scheduler we evaluate the impact of redundant requests on (1) average job performance, (2) schedule fairness, (3) system load, and (4) system predictability. We find that some of the popularly held beliefs about the harmfulness of redundant batch requests are unfounded. We also find that the two most critical issues with redundant requests are the additional load on current middleware infrastructures and unfairness towards users who do not use redundant requests. Using our experimental results we quantify both impacts in terms of the number of users who use redundant requests and of the amount of request redundancy these users employ. This work was supported by the NSF under Award 0546688.     

A privacy self-assessment framework for online social networks

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection “weapons” are the users themselves. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. Moreover, even when social networking platforms allow their participants to control the privacy level of every published item, adopting a correct privacy policy is often an annoying and frustrating task and many users prefer to adopt simple but extreme strategies such as “visible-to-all” (exposing themselves to the highest risk), or “hidden-to-all” (wasting the positive social and economic potential of social networking websites). In this paper we propose a theoretical framework to i) measure the privacy risk of the users and alert them whenever their privacy is compromised and ii) help the users customize semi-automatically their privacy settings by limiting the number of manual operations. By investigating the relationship between the privacy measure and privacy preferences of real Facebook users, we show the effectiveness of our framework.     

Summarizing dynamic Social Tagging Systems

Social tagging is a popular method that allows users of social networks to share annotation in the form of keywords, called tags, assigned to resources. Social tagging addresses information overload by easing the task of locating interesting entities in a social network. Nevertheless, users can still be overwhelmed by too many tags posted at each moment. A process is needed that offers an accurate overview of the representative entities and their relationships with each other, while dealing with the dynamics of social tagging and of tags’ semantics. We propose a method for the automated summarization of an evolving multi-modal social network, focusing on the entities that stay representative over time for some subnetwork in the social tagging system. We report on experiments with real data from the Bibsonomy social tagging system, where we compare our dynamic approach with a static one.     

Cluster-group based trusted computing for mobile social networks using implicit social behavioral graph

Mobile Social Networks (MSNs) facilitate connections between mobile devices, and are capable of providing an effective mobile computing environment for users to access, share, and distribute information. However, MSNs are virtual social spaces, the available information may not be trustworthy to all. Therefore, trust inference plays a critical role for establishing social links between mobile users. In MSNs, users’ transactions will more and more be complemented with group contact. Hence, future usage patterns of mobile devices will involve more group contacts. In this paper, we describe the implicit social behavioral graph, i.e., ego-i graph which is formed by users’ contacts, and present an algorithm for initiating ego-i graph. We rate these relationships to form a dynamic contact rank, which enables users to evaluate the trust values between users within the context of MSNs. We, then, calculate group-based trust values according to the level of contacts, interaction evolution, and users’ attributes. Based on group-based trust, we obtain a cluster trust by the aggregation of inter group-based trust values. Due to the unique nature of MSNs, we discuss the propagation of cluster trust values for global MSNs. Finally, we evaluate the performance of our trust model through simulations, and the results demonstrate the effectiveness of group-based behavioural relationships in MSNs’ information sharing system.     

Semantic tagging and linking of software engineering social content

Social online communities and platforms play a significant role in the activities of software developers either as an integral part of the main activities or through complimentary knowledge and information sharing. As such techniques become more prevalent resulting in a wealth of shared information, the need to effectively organize and sift through the information becomes more important. Top-down approaches such as formal hierarchical directories have shown to lack scalability to be applicable to these circumstanes. Light-weight bottom-up techniques such as community tagging have shown promise for better organizing the available content. However, in more focused communities of practice, such as software engineering and development, community tagging can face some challenges such as tag explosion, locality of tags and interpretation differences, to name a few. To address these challenges, we propose a semantic tagging approach that benefits from the information available in Wikipedia to semantically ground the tagging process and provide a methodical approach for tagging social software engineering content. We have shown that our approach is able to provide high quality tags for social software engineering content that can be used not only for organizing such content but also for making meaningful and relevant content recommendation to the users both within a local community and also across multiple social online communities. We have empirically validated our approach through four main research questions. The results of our observations show that the proposed approach is quite effective in organizing social software engineering content and making relevant, helpful and novel content recommendations to software developers and users of social software engineering communities.     

Access and Sharing Contents Through the Social Network: A POMDP Approach

Users interact in the social network by exchanging useful information. The huge amount of traffic generated requires the design of an accurate model for good management. Hence, knowing the consumers' access patterns is of a great interest for content owners. We propose a study of the access and sharing patterns based on the content's popularity. We suppose that popularity is inferred by the observation of the number of views. Our goal is twofold. On the one hand, we aim to help users select the optimal action that allows content owners to decide to change or not the used social network to another to increase their profits and, on the other hand, to assist consumers, according to their area of interest, to decide to access or not a posted content. We establish a threshold structure of the optimal policies based on a trade‐off study between profits (money, access to useful information, etc.) and costs (time spent to edit or consult content, etc.). The experimental results of the proposed analytical model show that both owners and consumers maximize their utility by choosing the best strategies.