Design and analysis of a social botnet

Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today’s web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8 weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80%, (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today’s underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security.     

Not Even Past: Information Aging and Temporal Privacy in Online Social Networks

Online social networks (OSNs) make information accessible for unlimited periods and provide easy access to past information by arranging information in time lines or by providing sophisticated search mechanisms. Despite increased concerns over the privacy threat that is posed by digital memory, there is little knowledge about retrospective privacy: the extent to which the age of the exposed information affects sharing preferences. In this article, we investigate how information aging impacts users’ sharing preferences on Facebook. Our findings are based on a between-subjects experiment (n = 272), in which we measured the impact of time since first publishing an OSN post on its sharing preferences. Our results quantify how willingness to share is lower for older Facebook posts and show that older posts have lower relevancy to the user’s social network and are less representative of the user’s identity. We show that changes in the user’s social circles, the occurrence of significant life changes and a user’s young age are correlated with a further decrease in the willingness to keep sharing past information. We discuss our findings by juxtaposing digital memory theories and privacy theories and suggest a vision for mechanisms that can help users manage longitudinal privacy.     

Content delivery simulations supported by social network-awareness

In this study we conduct experiments on a modified content delivery simulation framework, as we aspire to compare miscellaneous policies for dynamic OSN-aware content delivery. The incorporation of an OSN-aware dynamic mechanism becomes indispensable for CDN services, since (i)significantly large proportion of Internet traffic results from -easily produced via online media services and transmitted over OSNs- bandwidth-intensive multimedia content and (ii)multimedia content providers, such as YouTube, often rely on ubiquitous Content Distribution Networks (CDNs) infrastructures. Our policies take patterns of user activity over OSNs and exploit geo-social properties of users participating in retransmissions of items over OSNs (social cascades), proceed to incorporate various caching schemes of the underlying infrastructure, different policies for the handling of OSN data and various approaches that take into account the efficient timing of prefetching. The simulation framework we introduce can serve as the basis of further parameterized content delivery experimentation that exploits information transmission over OSNs and decreases replication costs by selectively copying items to locations where items are likely to be consumed.     

Content delivery simulations supported by social network-awareness

In this study we conduct experiments on a modified content delivery simulation framework, as we aspire to compare miscellaneous policies for dynamic OSN-aware content delivery. The incorporation of an OSN-aware dynamic mechanism becomes indispensable for CDN services, since (i)significantly large proportion of Internet traffic results from -easily produced via online media services and transmitted over OSNs- bandwidth-intensive multimedia content and (ii)multimedia content providers, such as YouTube, often rely on ubiquitous Content Distribution Networks (CDNs) infrastructures. Our policies take patterns of user activity over OSNs and exploit geo-social properties of users participating in retransmissions of items over OSNs (social cascades), proceed to incorporate various caching schemes of the underlying infrastructure, different policies for the handling of OSN data and various approaches that take into account the efficient timing of prefetching. The simulation framework we introduce can serve as the basis of further parameterized content delivery experimentation that exploits information transmission over OSNs and decreases replication costs by selectively copying items to locations where items are likely to be consumed.     

Customized Share Level Monitoring System for Users in OSN-Third Party Applications

Preserving privacy of the user is a very critical requirement to be met with all the international laws like GDPR, California privacy protection act and many other bills in place. On the other hand, Online Social Networks (OSN) has a wide spread recognition among the users, as a means of virtual communication. OSN may also acts as an identity provider for both internal and external applications. While it provides a simplified identification and authentication function to users across multiple applications, it also opens the users to a new spectrum of privacy threats. The privacy breaches costs to the users as well as to the OSN. Despite paying millions of dollars as fine every year, the OSN has not done any significant changes, as data is the fuel and what it loses as fine is far less compared to the money OSN makes out of the shared data. In this work, we have discussed a wide range of possible privacy threats and solutions prevailing in OSN-Third Party Application (TPA) data sharing scenario. Our solution models the behavior of the user, as well as TPA and pinpoints the avenues of over sharing to the users, thereby limiting the privacy loss of the user.     

在线社会网络用户的信息分享行为预测研究

在线社会网络中信息的传播路径包含着用户对内容、来源等的偏好信息,研究运用信息的传播路径来预测用户信息分享行为的方法。基于传播路径的信息过滤能力研究了信息在网络中的传播过程和信息传播路径的转换方法。运用基于关联规则的分类算法对在线社会网络中的信息分享行为进行预测。以新浪微博为例对微博用户的转发行为进行了预测,结果表明该方法对在线社会网络中的活跃用户的信息分享行为的预测具有较好的效果。     

Exploring sharing patterns for video recommendation on YouTube-like social media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.     

Context-aware privacy-preserving access control for mobile computing

In mobile and pervasive computing applications, opportunistic connections allow co-located devices to exchange data directly. Keeping data sharing local enables large-scale cooperative applications and empowers individual users to control what and how information is shared. Supporting such applications requires runtime frameworks that allow them to manage the who, what, when, and how of access to resources. Existing frameworks have limited expressiveness and do not allow data owners to modulate the granularity of information released. In addition, these frameworks focus exclusively on security and privacy concerns of data providers and do not consider the privacy of data consumers. We present PADEC, a context-sensitive, privacy-aware framework that allows users to define rich access control rules over their resources and to attach levels of granularity to each rule. PADEC is also characterized by its expressiveness, allowing users to decide under which conditions should which information be shared. We provide a formal definition of PADEC and an implementation based on private function evaluation. Our evaluation shows that PADEC is more expressive than other mechanisms, protecting privacy of both consumers and providers.     

Assessment of subjective influence and trust with an online social network game

The deduction of influence and trust between two individuals only from objective data in online social networks (OSNs) is a rather vague approach. Subjective assessments via surveys produce better results, but are harder to conduct considering the vast amount of friendships of OSN users. This work presents a framework for personalized surveys on relationships in OSNs, which follows a gamification approach. A Facebook game was developed, which was used to subjectively assess social influence and interpersonal trust based on models from psychology. The results show that it is possible to obtain subjective opinions and (limited) objective data about relationships with an OSN game. Also an implicit assessment of influence and trust with subcategory questions is feasible in this case.     

Location privacy: going beyond K-anonymity,cloaking and anonymizers

With many location-based services, it is implicitly assumed that the location server receives actual users locations to respond to their spatial queries. Consequently, information customized to their locations, such as nearest points of interest can be provided. However, there is a major privacy concern over sharing such sensitive information with potentially malicious servers, jeopardizing users’ private information. The anonymity- and cloaking-based approaches proposed to address this problem cannot provide stringent privacy guarantees without incurring costly computation and communication overhead. Furthermore, they require a trusted intermediate anonymizer to protect user locations during query processing. This paper proposes a fundamental approach based on private information retrieval to process range and K-nearest neighbor queries, the prevalent queries used in many location-based services, with stronger privacy guarantees compared to those of the cloaking and anonymity approaches. We performed extensive experiments on both real-world and synthetic datasets to confirm the effectiveness of our approaches.     

Privacy as information access and illusory control: The case of the Facebook News Feed privacy outcry

Increasingly, millions of people, especially youth, post personal information in online social networks (OSNs). In September 2006, one of the most popular sites—Facebook.com—introduced the features of News Feed and Mini Feed, revealing no more information than before, but resulting in immediate criticism from users. To investigate the privacy controversy, we conducted a survey among 172 current Facebook users in a large US university to explore their usage behaviors and privacy attitudes toward the introduction of the controversial News Feed and Mini Feed features. We examined the degree to which users were upset by the changes, explored the reasons as to why, and examined the influences of the News Feed privacy outcry on user behavior changes. The results have demonstrated how an easier information access and an “illusory” loss of control prompted by the introduction of News Feed features, triggered users’ privacy concerns. In addition to enhancing our theoretical understanding of privacy issues in the online social networks, this research is also potentially useful to privacy advocates, regulatory bodies, service providers, and marketers to help shape or justify their decisions concerning the online social networks.     

Effect of online social networking on student academic performance

Online social networks (OSNs) have permeated all generations of Internet users, becoming a prominent communications tool, particularly in the student community. Thus, academic institutions and faculty are increasingly using social networking sites, such as Facebook and LinkedIn, to connect with current and potential students and to deliver instructional content. This has led to a rise in questions about the impact of OSN on academic performance and the possibility of using it as an effective teaching tool. To learn more about the impact on academic performance, we conducted a survey of business students at a large state university. Survey results were analyzed using structural equation modeling (SEM). The results revealed a statistically significant negative relationship between time spent by students on OSN and their academic performance. The time spent on OSN was found to be heavily influenced by the attention span of the students. Specifically, we determined that the higher the attention span, the lower is the time spent on OSN. Further, attention span was found to be highly correlated with characteristics that predict or influence student behavior, such as their perceptions about society’s view of social networking, their likes and dislikes of OSN, ease of use of OSN, etc.     

Why would online gamers share their innovation-conducive knowledge in the online game user community? Integrating individual motivations and social capital perspectives

The user community has been an important external source of a firm’s product or service innovation. Users’ innovation-conducive knowledge sharing enables the community to work as a vital source of innovation. But, traditional economic theories of innovation seem to provide few explanations about why such knowledge sharing takes place for free in the user community. Therefore, this study investigates what drives community users to freely share their innovation-conducive knowledge, using the theory of planned behavior. Based on an empirical analysis of the data from 1244 members of a South Korean online game user community, it reveals that intrinsic motivation, shared goals, and social trust are salient factors in promoting users’ innovation-conducive knowledge sharing. Extrinsic motivation and social tie, however, were found to affect such sharing adversely, contingent upon whether a user is an innovator or a non-innovator. The study illustrates how social capital, in addition to individual motivations, forms and influences users’ innovation-conducive knowledge sharing in the online gaming context.     

The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China

The long-term development of social networking sites (SNSs) relies on their users’ continuance of use. This paper tested the role of perceived social capital and flow experience in driving users to continue using SNSs based on the expectation–confirmation model of information systems continuance. Using data from online surveys of social networking sites users in China, the proposed model was tested by using the Partial Least Squares (PLSs) technique. The results show that perceived bridging and bonding social capital have different role in building users’ satisfaction and continuance intention: perceived bridging social capital has remarkable influence on users’ satisfaction and continuance intention, but perceived bonding social capital has none. Flow experience has influence on users’ satisfaction but none on continuance intention. In addition, gender has impact on users’ continuance intention.     

Policing as a Service in the Cloud

ABSTRACT

Security and privacy are fundamental concerns in cloud computing both in terms of legal complications and user trust. Cloud computing is a new computing paradigm, aiming to provide reliable, customized, and guaranteed computing dynamic environment for end users. However, the existing security and privacy issues in the cloud still present a strong barrier for users to adopt cloud computing solutions. This paper investigates the security and privacy challenges in cloud computing in order to explore methods that improve the users’ trust in the adaptation of the cloud. Policing as a Service can be offered by the cloud providers with the intention of empowering users to monitor and guard their assets in the cloud. This service is beneficial both to the cloud providers and the users. However, at first, the cloud providers may only be able to offer basic auditing services due to undeveloped tools and applications. Similar to other services delivered in the cloud, users can purchase this service to gain some control over their data. The subservices of the proposed service can be Privacy as a Service and Forensics as a Service. These services give users a sense of transparency and control over their data in the cloud while better security and privacy safeguards are sought.     

微博用户的个性分类分析

社交网络给每个社会中的人提供了自由表达个人情感、观点、兴趣、建议等言论的平台。用户在这些平台上发表的言论、所做的行为以及用户在平台上建立的社交圈子也给数据挖掘带来了新的数据和机会。提出了一种利用用户在微博上的公开数据信息实现对该用户的MBTI个性维度进行分类分析的方法。在该方法中,基于对用户微博数据的分析,提出了能够表征用户心理和行为的文本和非文本特征,然后采用三种机器学习的分类方法—提升决策树、支持向量机和贝叶斯逻辑递归来对微博用户的个性进行分类分析。实验结果表明,通过对微博数据的挖掘可以在不同MBTI个性维度上达到75%~90%的准确率。     

Interplay between trust,information privacy concerns and behavioural intention of users on online social networks

Trust and privacy features of websites have evolved as an important concern for any businesses or interactions, particularly in online networks. The study investigates the relationship between trust, privacy concerns and behavioural intention of users on the social network. The behavioural intention of users on the online social network (OSN) is captured by intention to disclose information and intention to interact with others in OSN. The study was conducted on a sample of 457 active users from one of the major social networking website, Facebook. Partial least squares based structural equation modelling was used for analysing the results. The findings of the study reveal that intention to disclose information mediates the relationship between trust in the website and the intention to interact with others. Another important finding of the study indicates that prior positive experience with the website significantly impacts the trust in website, and the trust in website also plays a crucial role while determining the information privacy concerns in the OSN.     

The “Panopticon” of search engines: the response of the European data protection framework

Nowadays, Internet users are depending on various search engines in order to be able to find requested information on the Web. Although most users feel that they are and remain anonymous when they place their search queries, reality proves otherwise. The increasing importance of search engines for the location of the desired information on the Internet usually leads to considerable inroads into the privacy of users. A heated debate is currently ongoing at European level regarding the question if search engine providers that are established outside the European Union are covered by the European data protection framework and the obligations it imposes on entities that process personal data. The scope of this paper is to examine the applicability of the European data protection legislation to non-EU-based search engine providers and to study the main privacy issues with regard to search engines, such as the character of search logs, their anonymisation and their retention period. Ixquick, a privacy-friendly meta-search engine, will be presented as an alternative to privacy intrusive existing practices of search engines.     

Understanding the behavior changes in belief and attitude among experienced and inexperienced learning object users

Prior empirical studies in the implementation of general information technologies (IT) have revealed that IT adoption and usage were determined by user beliefs and attitudes. However, little is known about how user beliefs and attitudes form and change over time. To address these issues, this paper reports a study of 481 inexperienced and 120 experienced potential users on learning objects. Technology acceptance model’s constructs were used to conduct a longitudinal study across three phases (introduction, training and direct-use experience) to examine the formation and the changes in users’ beliefs and behavioral intention to use learning objects over time. The results showed that the rates of changes in users’ beliefs and behavioral intention toward learning objects usage were time-variant and were more predominant during the early stage (introduction to training) of learning objects usage than in the later stages (training to direct-use experience). The study confirmed that initial exposure through introduction and training was effective in improving inexperienced users’ beliefs and intentions to use learning objects. It also helped to reduce the belief and intention gaps that existed between experienced and inexperienced users. In addition, the influence of initial introduction and training on users’ beliefs and perceptions was sustained over time, thus further indicating their importance.     

Ensuring efficient multimedia message sharing in mobile social network

Mobile Social Networks (MSN) are attractive applications which enable users to share data with a group of friends and stay connected. WeChat, QQ are among the most popular applications of MSNs where personal multimedia files are shared among group contacts. However, the security risks accompanying such developments have raised concerns in people. The providers typically store users’ data, and offer few options for the users to custom and manage the dissipation of their data over the network. In this paper, we design a data sharing framework in which individuals retain ownership of their data. The scheme gives users flexible and granular access control over their data, and more importantly it provides protection from the untrusted data provider server. Experiments show the efficiency of our scheme.