Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.66
Ruoyu Wu, Xinwen Zhang, Gail-Joon Ahn, Hadi Sharifi, Haiyong Xie
Organizations and enterprises have been outsourcing their computation, storage, and workflows to Infrastructure-as-a-Service (IaaS) based cloud platforms. The heterogeneity and high diversity of IaaS cloud environment demand a comprehensive and fine-grained access control mechanism, in order to meet dynamic, extensible, and highly configurable security requirements of these cloud consumers. However, existing security mechanisms provided by IaaS cloud providers do not satisfy these requirements. To address such an emergent demand, we propose a new cloud service called access control as a service (ACaaS), a service-oriented architecture in cloud to support multiple access control models, with the spirit of plug gable access control modules in modern operating systems. As a proof-of-concept reference prototype, we design and implement ACaaS_RBAC to provide role-based access control (RBAC) for Amazon Web Services (AWS), where cloud customers can easily integrate the service into enterprise applications in order to extend RBAC policy enforcement in AWS.
组织和企业已经将其计算、存储和工作流外包给基于基础设施即服务(IaaS)的云平台。IaaS云环境的异构性和高度多样性，需要一种全面、细粒度的访问控制机制，以满足这些云消费者动态、可扩展、高度可配置的安全需求。然而，IaaS云提供商提供的现有安全机制并不能满足这些需求。为了满足这种需求，我们提出了一种新的云服务——访问控制即服务(ACaaS)，这是一种面向服务的云架构，支持多种访问控制模型，具有现代操作系统中插拔式访问控制模块的精神。作为概念验证参考原型，我们设计并实现了ACaaS_RBAC，为Amazon Web Services (AWS)提供基于角色的访问控制(RBAC)，云客户可以轻松地将该服务集成到企业应用程序中，以便在AWS中扩展RBAC策略的实施。
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Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.87
Michael Hanspach, J. Keller
With the rise of formally verified micro kernels, we finally have a trusted platform for secure IPC and rigorous enforcement of our mandatory access control policy. But, not every problem in computer security and privacy could possibly be solved by a trusted micro kernel, because we have higher level security and privacy concepts like packet filtering, data encryption and partitioning of shared hardware devices, which we also need to trust. Numerous authors have described the need for a trusted middleware, fulfilling these higher level security and privacy goals, but detailed requirements for the different security and privacy goals are still missing. We provide a collection of output filters that can be applied to trusted operating system components to enforce higher level security goals. We further provide a typology of operating system guards, which are essentially trusted components utilizing different compilations of input and output filters. The storage guard, the audio filtering guard and the sequencing guard are specifically targeted at providing solutions to three common security and privacy problems in component-based operating systems. Finally, we develop a guard reference architecture and present the concept of a guard construction kit for the development of new types of operating system guards, enabling operating system developers to build their own guard components for both component-based and commodity operating systems.
{"title":"In Guards We Trust: Security and Privacy in Operating Systems Revisited","authors":"Michael Hanspach, J. Keller","doi":"10.1109/SocialCom.2013.87","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.87","url":null,"abstract":"With the rise of formally verified micro kernels, we finally have a trusted platform for secure IPC and rigorous enforcement of our mandatory access control policy. But, not every problem in computer security and privacy could possibly be solved by a trusted micro kernel, because we have higher level security and privacy concepts like packet filtering, data encryption and partitioning of shared hardware devices, which we also need to trust. Numerous authors have described the need for a trusted middleware, fulfilling these higher level security and privacy goals, but detailed requirements for the different security and privacy goals are still missing. We provide a collection of output filters that can be applied to trusted operating system components to enforce higher level security goals. We further provide a typology of operating system guards, which are essentially trusted components utilizing different compilations of input and output filters. The storage guard, the audio filtering guard and the sequencing guard are specifically targeted at providing solutions to three common security and privacy problems in component-based operating systems. Finally, we develop a guard reference architecture and present the concept of a guard construction kit for the development of new types of operating system guards, enabling operating system developers to build their own guard components for both component-based and commodity operating systems.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127206772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.174
Chang-Hoo Jeong, Sung-Pil Choi, Sungho Shin, Seungwoo Lee, Hanmin Jung, Soon-Young Kim, Pyung Kim
The semantic web technology contributes to finding accurate information on the basis of the meaning of words or improving access capabilities to information through inference between meanings. However, the reason that the semantic service is not spreading is that the semantic technology is not yet settled for practical use. Another reason is that it is not easy to build ontology which is used as a knowledge representation model for providing the semantic service. Previous studies have concentrated on the process of automatically creating ontology in a RDB (Relational Database), or automatically converting it to instances. This study focuses on allocating a unique identifier to the existing DB data through entity identification to enable more accurate services to be provided in the process of converting the RDBMS data to ontology instances. Besides, this study extracts triple data including important entities and their relationships from unstructured texts stored in BLOB (Binary Large Object) format by using text mining technology, and makes them ontology instances. The DB-to-OWL converting system (Onto URI) uses a mapping rule between the DB schema and the ontology schema, an identification rule for identifying entities, text mining for extracting semantic triple, and the authority data in order to effectively support automatic creation of ontology instances with DB data. As a result, the proposed system contributes to creating semantic data automatically from relational database through URI allocation and identification.
语义网技术有助于根据词的含义找到准确的信息，或通过意义之间的推理提高对信息的访问能力。然而，语义服务没有得到推广的原因是语义技术还没有得到实际应用。另一个原因是本体不容易建立，本体作为提供语义服务的知识表示模型。以往的研究主要集中在RDB(关系型数据库)中本体的自动创建过程，或者将本体自动转换为实例。本研究的重点是通过实体识别为现有的DB数据分配一个唯一的标识符，以便在将RDBMS数据转换为本体实例的过程中提供更准确的服务。此外，利用文本挖掘技术从存储于BLOB (Binary Large Object，二进制大对象)格式的非结构化文本中提取包含重要实体及其关系的三重数据，并将其作为本体实例。数据库到owl的转换系统(Onto URI)使用数据库模式和本体模式之间的映射规则、标识实体的标识规则、提取语义三元组的文本挖掘和权威数据，有效地支持用DB数据自动创建本体实例。通过URI分配和标识，实现了从关系数据库中自动生成语义数据的功能。
{"title":"Creating Semantic Data from Relational Database","authors":"Chang-Hoo Jeong, Sung-Pil Choi, Sungho Shin, Seungwoo Lee, Hanmin Jung, Soon-Young Kim, Pyung Kim","doi":"10.1109/SocialCom.2013.174","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.174","url":null,"abstract":"The semantic web technology contributes to finding accurate information on the basis of the meaning of words or improving access capabilities to information through inference between meanings. However, the reason that the semantic service is not spreading is that the semantic technology is not yet settled for practical use. Another reason is that it is not easy to build ontology which is used as a knowledge representation model for providing the semantic service. Previous studies have concentrated on the process of automatically creating ontology in a RDB (Relational Database), or automatically converting it to instances. This study focuses on allocating a unique identifier to the existing DB data through entity identification to enable more accurate services to be provided in the process of converting the RDBMS data to ontology instances. Besides, this study extracts triple data including important entities and their relationships from unstructured texts stored in BLOB (Binary Large Object) format by using text mining technology, and makes them ontology instances. The DB-to-OWL converting system (Onto URI) uses a mapping rule between the DB schema and the ontology schema, an identification rule for identifying entities, text mining for extracting semantic triple, and the authority data in order to effectively support automatic creation of ontology instances with DB data. As a result, the proposed system contributes to creating semantic data automatically from relational database through URI allocation and identification.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125888276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.11
J. Tsai, Yundi Qian, Yevgeniy Vorobeychik, Christopher Kiekintveld, Milind Tambe
Influence blocking games have been used to model adversarial domains with a social component, such as counterinsurgency. In these games, a mitigator attempts to minimize the efforts of an influencer to spread his agenda across a social network. Previous work has assumed that the influence graph structure is known with certainty by both players. However, in reality, there is often significant information asymmetry between the mitigator and the influencer. We introduce a model of this information asymmetry as a two-player zero-sum Bayesian game. Nearly all past work in influence maximization and social network analysis suggests that graph structure is fundamental in strategy generation, leading to an expectation that solving the Bayesian game exactly is crucial. Surprisingly, we show through extensive experimentation on synthetic and real-world social networks that many common forms of uncertainty can be addressed near optimally by ignoring the vast majority of it and simply solving an abstracted game with a few randomly chosen types. This suggests that optimal strategies of games that do not model the full range of uncertainty in influence blocking games are typically robust to uncertainty about the influence graph structure.
{"title":"Bayesian Security Games for Controlling Contagion","authors":"J. Tsai, Yundi Qian, Yevgeniy Vorobeychik, Christopher Kiekintveld, Milind Tambe","doi":"10.1109/SocialCom.2013.11","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.11","url":null,"abstract":"Influence blocking games have been used to model adversarial domains with a social component, such as counterinsurgency. In these games, a mitigator attempts to minimize the efforts of an influencer to spread his agenda across a social network. Previous work has assumed that the influence graph structure is known with certainty by both players. However, in reality, there is often significant information asymmetry between the mitigator and the influencer. We introduce a model of this information asymmetry as a two-player zero-sum Bayesian game. Nearly all past work in influence maximization and social network analysis suggests that graph structure is fundamental in strategy generation, leading to an expectation that solving the Bayesian game exactly is crucial. Surprisingly, we show through extensive experimentation on synthetic and real-world social networks that many common forms of uncertainty can be addressed near optimally by ignoring the vast majority of it and simply solving an abstracted game with a few randomly chosen types. This suggests that optimal strategies of games that do not model the full range of uncertainty in influence blocking games are typically robust to uncertainty about the influence graph structure.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"71 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123251310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.85
Peter J. Radics, D. Gračanin, D. Kafura
With the increased ubiquity of technology comes the challenge of addressing the privacy concerns of the intended users. Privacy is an extremely complex social phenomenon with myriads of variations. Difficulty in designing privacy-aware applications, however, do not stem from a lack of models or design frameworks. Rather, it stems from the gap between the two, the lack of guidance in eliciting privacy requirements. To address this issue, we introduce the Preprocess, a methodological framework for privacy requirements engineering, and demonstrate its power through an application example.
{"title":"Preprocess before You Build: Introducing a Framework for Privacy Requirements Engineering","authors":"Peter J. Radics, D. Gračanin, D. Kafura","doi":"10.1109/SocialCom.2013.85","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.85","url":null,"abstract":"With the increased ubiquity of technology comes the challenge of addressing the privacy concerns of the intended users. Privacy is an extremely complex social phenomenon with myriads of variations. Difficulty in designing privacy-aware applications, however, do not stem from a lack of models or design frameworks. Rather, it stems from the gap between the two, the lack of guidance in eliciting privacy requirements. To address this issue, we introduce the Preprocess, a methodological framework for privacy requirements engineering, and demonstrate its power through an application example.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127036300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.52
Akshay Patil, Juan Liu, Jianqiang Shen, Oliver Brdiczka, Jie Gao, J. Hanley
Modeling people's online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacy-preserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people's online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65% on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging.
{"title":"Modeling Attrition in Organizations from Email Communication","authors":"Akshay Patil, Juan Liu, Jianqiang Shen, Oliver Brdiczka, Jie Gao, J. Hanley","doi":"10.1109/SocialCom.2013.52","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.52","url":null,"abstract":"Modeling people's online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacy-preserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people's online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65% on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115581252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.94
Sharon Paradesi, Ilaria Liccardi, Lalana Kagal, J. Pato
Social networking sites provide role-or group-based access controls to help users specify their privacy settings. However, information posted on these sites is often intentionally or unintentionally leaked and has caused harm or distress to users. In this paper, we investigate possible improvements to existing implementations by introducing content-based access control policies using Linked Data. Users are able to specify the type of content in the form of tags or keywords in order to indicate which information they wish to protect from certain roles (for example employment), groups or individuals. Providing all possible keywords matching a specific topic may be too time consuming and prone to error for users. Hence using Linked Data we enrich the provided keywords by identifying other meaningful and related concepts. This paper presents the implementation and challenges of developing such a semantic framework. We have qualitatively evaluated this framework using 23 participants. Feedback from participants suggests that such a framework will help ease privacy concerns while posting and sharing social network content.
{"title":"A Semantic Framework for Content-Based Access Controls","authors":"Sharon Paradesi, Ilaria Liccardi, Lalana Kagal, J. Pato","doi":"10.1109/SocialCom.2013.94","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.94","url":null,"abstract":"Social networking sites provide role-or group-based access controls to help users specify their privacy settings. However, information posted on these sites is often intentionally or unintentionally leaked and has caused harm or distress to users. In this paper, we investigate possible improvements to existing implementations by introducing content-based access control policies using Linked Data. Users are able to specify the type of content in the form of tags or keywords in order to indicate which information they wish to protect from certain roles (for example employment), groups or individuals. Providing all possible keywords matching a specific topic may be too time consuming and prone to error for users. Hence using Linked Data we enrich the provided keywords by identifying other meaningful and related concepts. This paper presents the implementation and challenges of developing such a semantic framework. We have qualitatively evaluated this framework using 23 participants. Feedback from participants suggests that such a framework will help ease privacy concerns while posting and sharing social network content.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129943065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.151
Yue Ming
The times of Big Data promotes increasingly higher demands for information processing. The rapid development of 3D digital capturing devices prompts the traditional behavior analysis towards fine motion recognition, such as hands and gesture. In this paper, a complete framework of 3D human activity recognition is presented for the behavior analysis of hands and gesture. First, the improved graph cuts method is introduced to hand segmentation and tracking. Then, combined with 3D geometric characteristics and human behavior prior information, 3D Mesh MoSIFT feature descriptor is proposed to represent the discriminant property of human activity. Simulation orthogonal matching pursuit (SOMP) is used to encode the visual code words. Experiments, based on a RGB-D video dataset and ChaLearn gesture dataset, show the improved accuracy of human activity recognition.
{"title":"Human Activity Recognition Based on 3D Mesh MoSIFT Feature Descriptor","authors":"Yue Ming","doi":"10.1109/SocialCom.2013.151","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.151","url":null,"abstract":"The times of Big Data promotes increasingly higher demands for information processing. The rapid development of 3D digital capturing devices prompts the traditional behavior analysis towards fine motion recognition, such as hands and gesture. In this paper, a complete framework of 3D human activity recognition is presented for the behavior analysis of hands and gesture. First, the improved graph cuts method is introduced to hand segmentation and tracking. Then, combined with 3D geometric characteristics and human behavior prior information, 3D Mesh MoSIFT feature descriptor is proposed to represent the discriminant property of human activity. Simulation orthogonal matching pursuit (SOMP) is used to encode the visual code words. Experiments, based on a RGB-D video dataset and ChaLearn gesture dataset, show the improved accuracy of human activity recognition.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128633342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.140
T. Hughes-Roberts
End-user privacy is a well-defined problem in Social Networks such as Facebook. Users have stated concern for their privacy yet display behaviour to the contrary within the system, a phenomenon known as the privacy paradox. There is an assumption that high levels of concern for one's privacy should lead to a reluctance to disclose information and to an acceptance of highly protective measures within the network. Few works have studied this paradox in its entirety, each taking differing views of it and using a variety of measures. Furthermore, evidence for the paradox has been found between varying conceptual elements of it and hence, there is a need for a holistic study of this phenomenon in order to identify where the paradox manifests. This work implements a survey instrument aimed at examining if concern should be used as an indicator of intention and action as it is assumed in past literature. Results show that users of social networks desire a benchmark of privacy that is consistent across measures of concern. A survey instrument examining general statements of concern would appear to inadequate in exploring the complex nature of privacy in social networks.
{"title":"Privacy and Social Networks: Is Concern a Valid Indicator of Intention and Behaviour?","authors":"T. Hughes-Roberts","doi":"10.1109/SocialCom.2013.140","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.140","url":null,"abstract":"End-user privacy is a well-defined problem in Social Networks such as Facebook. Users have stated concern for their privacy yet display behaviour to the contrary within the system, a phenomenon known as the privacy paradox. There is an assumption that high levels of concern for one's privacy should lead to a reluctance to disclose information and to an acceptance of highly protective measures within the network. Few works have studied this paradox in its entirety, each taking differing views of it and using a variety of measures. Furthermore, evidence for the paradox has been found between varying conceptual elements of it and hence, there is a need for a holistic study of this phenomenon in order to identify where the paradox manifests. This work implements a survey instrument aimed at examining if concern should be used as an indicator of intention and action as it is assumed in past literature. Results show that users of social networks desire a benchmark of privacy that is consistent across measures of concern. A survey instrument examining general statements of concern would appear to inadequate in exploring the complex nature of privacy in social networks.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129249894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-09-08DOI: 10.1109/SocialCom.2013.36
Ying Chen, Sencun Zhu, Heng Xu, Yilu Zhou
There is a rising concern among parents that mobile advertisements may contain violent and sexual content even when the app itself is safe for children. Because mobile advertisements are not controlled by the content rating of apps, unexpected objectionable contents may occur and be harmful to children's mental health. This study is the first to explore the content appropriateness of the in-app advertisements on mobile devices from children's online safety perspective. We find in-app advertisements are common in the free apps designed for children on smart platforms. Experimental results show that a large percent of the in-app advertisements carry inappropriate contents for children. Unfortunately, neither mobile platforms nor advertising networks provide maturity policies to restrict the content appropriateness of the in-app advertisements. This research suggests that these challenges cannot easily be tackled by one entity. Instead, advertisement providers, advertising networks, app developers, and mobile platforms should collaborate in developing policies and mechanisms to monitor the content appropriateness of the in-app advertisements.
{"title":"Children's Exposure to Mobile In-App Advertising: An Analysis of Content Appropriateness","authors":"Ying Chen, Sencun Zhu, Heng Xu, Yilu Zhou","doi":"10.1109/SocialCom.2013.36","DOIUrl":"https://doi.org/10.1109/SocialCom.2013.36","url":null,"abstract":"There is a rising concern among parents that mobile advertisements may contain violent and sexual content even when the app itself is safe for children. Because mobile advertisements are not controlled by the content rating of apps, unexpected objectionable contents may occur and be harmful to children's mental health. This study is the first to explore the content appropriateness of the in-app advertisements on mobile devices from children's online safety perspective. We find in-app advertisements are common in the free apps designed for children on smart platforms. Experimental results show that a large percent of the in-app advertisements carry inappropriate contents for children. Unfortunately, neither mobile platforms nor advertising networks provide maturity policies to restrict the content appropriateness of the in-app advertisements. This research suggests that these challenges cannot easily be tackled by one entity. Instead, advertisement providers, advertising networks, app developers, and mobile platforms should collaborate in developing policies and mechanisms to monitor the content appropriateness of the in-app advertisements.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126350893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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