The Experts below are selected from a list of 173286 Experts worldwide ranked by ideXlab platform
Ting Yu - One of the best experts on this subject based on the ideXlab platform.
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Protecting sensitive labels in Social Network Data anonymization
IEEE Transactions on Knowledge and Data Engineering, 2013Co-Authors: Mingxuan Yuan, Philip S Yu, Lei Chen, Ting YuAbstract:Privacy is one of the major concerns when publishing or sharing Social Network Data for Social science research and business analysis. Recently, researchers have developed privacy models similar to k-anonymity to prevent node reidentification through structure information. However, even when these privacy models are enforced, an attacker may still be able to infer one's private information if a group of nodes largely share the same sensitive labels (i.e., attributes). In other words, the label-node relationship is not well protected by pure structure anonymization methods. Furthermore, existing approaches, which rely on edge editing or node clustering, may significantly alter key graph properties. In this paper, we define a k-degree-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals. We further propose a novel anonymization methodology based on adding noise nodes. We develop a new algorithm by adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties. Most importantly, we provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. We conduct extensive experiments to evaluate the effectiveness of the proposed technique.
Tsan-sheng Hsu - One of the best experts on this subject based on the ideXlab platform.
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RSCTC - A grc-based approach to Social Network Data protection
Rough Sets and Current Trends in Computing, 2006Co-Authors: Da-wei Wang, Churn-jung Liau, Tsan-sheng HsuAbstract:Social Network analysis is an important methodology in sociological research. Although Social Network Data is very useful to researchers and policy makers, releasing it to the public may cause an invasion of privacy. In this paper, we generalize the techniques used to protect private information in tabulated Data, and propose some safety criteria for assessing the risk of breaching confidentiality by releasing Social Network Data. We assume a situation of Data linking, where Data is released to a particular user who has some knowledge about individual nodes of a Social Network. We adopt description logic as the underlying knowledge representation formalism and consider the safety criteria in both open-world and closed-world contexts.
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FUZZ-IEEE - Privacy Protection in Social Network Data Disclosure Based on Granular Computing
2006 IEEE International Conference on Fuzzy Systems, 2006Co-Authors: Da-wei Wang, Churn-jung Liau, Tsan-sheng HsuAbstract:Social Network analysis is an important methodology in sociological research. Though Social Network Data is very useful to researchers and policy makers, releasing such Data to the public may cause an invasion of privacy. We generalize the techniques for protecting personal privacy in tabulated Data, and propose some metrics of anonymity for assessing the risk of breaching confidentiality by disclosing Social Network Data. We assume a situation of Data publication, where Data is released to the general public. We adopt description logic as the underlying knowledge representation formalism, and consider the metrics of anonymity in open world and closed world contexts respectively.
Selwyn Piramuthu - One of the best experts on this subject based on the ideXlab platform.
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dynamic organizational learning with iot and retail Social Network Data
Hawaii International Conference on System Sciences, 2016Co-Authors: Wei Zhou, Frederique Alexandrebailly, Selwyn PiramuthuAbstract:We propose an effective mechanism based on mixed retail Social Network and Internet of Things (IoT) Data for optimal organizational learning. Based on the classical routine-based knowledge presumption in traditional organizational learning that is sub-optimal, we propose a self-adaptive knowledge base for dynamic organizational learning through IoT and Social Network Data. We use a Bayesian Network model to visualize and manage knowledge formation of all relevant dimensions for organizational learning. Our preliminary results show that IoT and retail Social Network Data analytics have a profound impact on firms from marketing to everyday organizational management and organizational knowledge accumulation.
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HICSS - Dynamic Organizational Learning with IoT and Retail Social Network Data
2016 49th Hawaii International Conference on System Sciences (HICSS), 2016Co-Authors: Wei Zhou, Frederique Alexandre-bailly, Selwyn PiramuthuAbstract:We propose an effective mechanism based on mixed retail Social Network and Internet of Things (IoT) Data for optimal organizational learning. Based on the classical routine-based knowledge presumption in traditional organizational learning that is sub-optimal, we propose a self-adaptive knowledge base for dynamic organizational learning through IoT and Social Network Data. We use a Bayesian Network model to visualize and manage knowledge formation of all relevant dimensions for organizational learning. Our preliminary results show that IoT and retail Social Network Data analytics have a profound impact on firms from marketing to everyday organizational management and organizational knowledge accumulation.
Mingxuan Yuan - One of the best experts on this subject based on the ideXlab platform.
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Protecting sensitive labels in Social Network Data anonymization
IEEE Transactions on Knowledge and Data Engineering, 2013Co-Authors: Mingxuan Yuan, Philip S Yu, Lei Chen, Ting YuAbstract:Privacy is one of the major concerns when publishing or sharing Social Network Data for Social science research and business analysis. Recently, researchers have developed privacy models similar to k-anonymity to prevent node reidentification through structure information. However, even when these privacy models are enforced, an attacker may still be able to infer one's private information if a group of nodes largely share the same sensitive labels (i.e., attributes). In other words, the label-node relationship is not well protected by pure structure anonymization methods. Furthermore, existing approaches, which rely on edge editing or node clustering, may significantly alter key graph properties. In this paper, we define a k-degree-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals. We further propose a novel anonymization methodology based on adding noise nodes. We develop a new algorithm by adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties. Most importantly, we provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. We conduct extensive experiments to evaluate the effectiveness of the proposed technique.
Sankita J. Patel - One of the best experts on this subject based on the ideXlab platform.
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a novel k anonymization approach to prevent insider attack in collaborative Social Network Data publishing
International Conference on Information Systems Security, 2019Co-Authors: Bintu Kadhiwala, Sankita J. PatelAbstract:Social Network Data analysts can retrieve improved results if mining operations are performed on collaborative Social Network Data instead of independent Social Network Data. The collaborative Social Network can be constructed by joining Data of all Social Networking sites. This Data may contain sensitive information about individuals in its original form and sharing of such Data, as it is, may violate individual privacy. Hence, various techniques are discussed in literature for privacy preserving publishing of Social Network Data. However, these techniques suffer from the insider attack, performed by colluding Data provider(s) to breach the privacy of the Social Network Data contributed by other Data providers. In this paper, we propose an approach that offers protection against the insider attack in the collaborative Social Network Data publishing scenario. Experimental results demonstrate that our approach preserves Data utility while protecting collaborated Social Network Data against the insider attack.
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ICISS - A Novel k-Anonymization Approach to Prevent Insider Attack in Collaborative Social Network Data Publishing
Information Systems Security, 2019Co-Authors: Bintu Kadhiwala, Sankita J. PatelAbstract:Social Network Data analysts can retrieve improved results if mining operations are performed on collaborative Social Network Data instead of independent Social Network Data. The collaborative Social Network can be constructed by joining Data of all Social Networking sites. This Data may contain sensitive information about individuals in its original form and sharing of such Data, as it is, may violate individual privacy. Hence, various techniques are discussed in literature for privacy preserving publishing of Social Network Data. However, these techniques suffer from the insider attack, performed by colluding Data provider(s) to breach the privacy of the Social Network Data contributed by other Data providers. In this paper, we propose an approach that offers protection against the insider attack in the collaborative Social Network Data publishing scenario. Experimental results demonstrate that our approach preserves Data utility while protecting collaborated Social Network Data against the insider attack.
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ISPEC - Privacy Preserving Approach in Dynamic Social Network Data Publishing
Information Security Practice and Experience, 2019Co-Authors: Kamalkumar R. Macwan, Sankita J. PatelAbstract:In recent years, Social Networks have gained special attention to share information and to maintain a relationship with other people. As the Data produced from such platforms are being analyzed, the privacy preservation methods must be applied before making the Data publicly available. The anonymization techniques consider one-time releases and do not re-publish the dynamic Social Network Data. The relationship between individuals changes with time so it may breach user privacy in dynamic Social Networks. In this paper, we propose an anonymization approach to preserve the user identity from all the published time-series Dataset of a Social Network.
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SPACE - Mutual Friend Attack Prevention in Social Network Data Publishing
Security Privacy and Applied Cryptography Engineering, 2017Co-Authors: Kamalkumar R. Macwan, Sankita J. PatelAbstract:Due to increasing demand of publishing Social Network Data, privacy has raised more concern for Data publisher. There are different risks and attacks still exist that can breach user privacy. Online Social Network such as Facebook, Google Plus and LinkedIn provide a feature that allows finding out number of mutual friends (NMF) between two users. Adversary can use such information to identify individual user and his/her connections. As published Dataset itself reveals mutual friends information for each connection, it becomes very easy for an adversary to re-identify the individual user.
