The Experts below are selected from a list of 26130 Experts worldwide ranked by ideXlab platform
Junguo Liao - One of the best experts on this subject based on the ideXlab platform.
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ICAIS (2) - Privacy Security Classification (PSC) Model for the Attributes of Social Network Users.
Lecture Notes in Computer Science, 2020Co-Authors: Yao Xiao, Junguo LiaoAbstract:With the development of technology and the increasing popularity of smart devices, more and more people use social network sites. When users express their opinions on the Internet, their personal Privacy may be inadvertently exposed, which make the Privacy Issue more obvious. At present, many existing studies consider methods to encrypt or calculate Privacy ratings without considering whether this is unreasonable for social networks. Meanwhile, these studies ignore the limitation of memory resources. In order to protect the Privacy of sensitive information with limited resources, classification of Privacy information is particularly important. In this paper, we discuss a Privacy security classification model for the attributes of online social network users. Through this method, the Privacy risk degree of user attributes on social network can be clearly understood. In addition, we introduce visibility innovatively into the Privacy security classification model and provide a reference for future research.
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Privacy security classification psc model for the attributes of social network users
International Conference on Adaptive and Intelligent Systems, 2020Co-Authors: Yao Xiao, Junguo LiaoAbstract:With the development of technology and the increasing popularity of smart devices, more and more people use social network sites. When users express their opinions on the Internet, their personal Privacy may be inadvertently exposed, which make the Privacy Issue more obvious. At present, many existing studies consider methods to encrypt or calculate Privacy ratings without considering whether this is unreasonable for social networks. Meanwhile, these studies ignore the limitation of memory resources. In order to protect the Privacy of sensitive information with limited resources, classification of Privacy information is particularly important. In this paper, we discuss a Privacy security classification model for the attributes of online social network users. Through this method, the Privacy risk degree of user attributes on social network can be clearly understood. In addition, we introduce visibility innovatively into the Privacy security classification model and provide a reference for future research.
Yao Xiao - One of the best experts on this subject based on the ideXlab platform.
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ICAIS (2) - Privacy Security Classification (PSC) Model for the Attributes of Social Network Users.
Lecture Notes in Computer Science, 2020Co-Authors: Yao Xiao, Junguo LiaoAbstract:With the development of technology and the increasing popularity of smart devices, more and more people use social network sites. When users express their opinions on the Internet, their personal Privacy may be inadvertently exposed, which make the Privacy Issue more obvious. At present, many existing studies consider methods to encrypt or calculate Privacy ratings without considering whether this is unreasonable for social networks. Meanwhile, these studies ignore the limitation of memory resources. In order to protect the Privacy of sensitive information with limited resources, classification of Privacy information is particularly important. In this paper, we discuss a Privacy security classification model for the attributes of online social network users. Through this method, the Privacy risk degree of user attributes on social network can be clearly understood. In addition, we introduce visibility innovatively into the Privacy security classification model and provide a reference for future research.
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Privacy security classification psc model for the attributes of social network users
International Conference on Adaptive and Intelligent Systems, 2020Co-Authors: Yao Xiao, Junguo LiaoAbstract:With the development of technology and the increasing popularity of smart devices, more and more people use social network sites. When users express their opinions on the Internet, their personal Privacy may be inadvertently exposed, which make the Privacy Issue more obvious. At present, many existing studies consider methods to encrypt or calculate Privacy ratings without considering whether this is unreasonable for social networks. Meanwhile, these studies ignore the limitation of memory resources. In order to protect the Privacy of sensitive information with limited resources, classification of Privacy information is particularly important. In this paper, we discuss a Privacy security classification model for the attributes of online social network users. Through this method, the Privacy risk degree of user attributes on social network can be clearly understood. In addition, we introduce visibility innovatively into the Privacy security classification model and provide a reference for future research.
Jessye Dos Santos - One of the best experts on this subject based on the ideXlab platform.
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Security Protocols and Privacy Issues into 6LoWPAN Stack: A Synthesis
IEEE Internet of Things Journal, 2014Co-Authors: Christine Hennebert, Jessye Dos SantosAbstract:With the emergence of the Internet of Things (IoT), many devices organized into network, communicate by themselves on the Internet, and send data or private information on the web. It is essential to secure the transmitted data and the identities that may be disclosed to make these new technologies accepted by the largest number of citizens. However, the security mechanisms that are widely used on the Internet are too heavy to be integrated on small constrained objects. This paper describes the current protocols and security solutions that can be deployed in constrained resources. It shows the benefits and the limitations of each scheme-the security extension of IEEE 802.15.4e in time-slotted channel hopping (TSCH) mode, compressed IPsec, datagram transport layer security (DTLS)-embedded at different levels of the OSI model into the 6LoWPAN stack. It opens with the challenge that one must tackle in the coming years. Several use cases are studied to envisage the security integration in cyber physical systems (CPSs) for host-to-host and host-to-network communications. The Privacy Issue is also addressed and different ways to hide the device identity are discussed.
R.m. Nelms - One of the best experts on this subject based on the ideXlab platform.
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Distributed Online Algorithm for Optimal Real-Time Energy Distribution in the Smart Grid
IEEE Internet of Things Journal, 2014Co-Authors: Yu Wang, Shiwen Mao, R.m. NelmsAbstract:In recent years, the smart grid has been recognized as an important form of the Internet of Things (IoT). The two-way energy and information flows in a smart gird, together with the smart devices, bring about new perspectives to energy management. This paper investigates a distributed online algorithm for electricity distribution in a smart grid environment. We first present a formulation that captures the key design factors such as user’s utility, grid load smoothing, and energy provisioning cost. The problem is shown to be convex and can be solved with a centralized online algorithm that only requires present information about users and the grid in our prior work. In this paper, we develop a distributed online algorithm that decomposes and solves the online problem in a distributed manner, and prove that the distributed online solution is asymptotically optimal. The proposed distributed online algorithm is also practical and mitigates the user Privacy Issue by not sharing user utility functions. It is evaluated with trace-driven simulations and shown to outperform a benchmark scheme.
Li Zhang - One of the best experts on this subject based on the ideXlab platform.
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ICCNS - Security Risk Estimation of Social Network Privacy Issue
Proceedings of the 2017 the 7th International Conference on Communication and Network Security - ICCNS 2017, 2017Co-Authors: Xueqin Zhang, Li ZhangAbstract:Users in social network are confronted with the risk of Privacy leakage while sharing information with friends whose Privacy protection awareness is poor. This paper proposes a security risk estimation framework of social network Privacy, aiming at quantifying Privacy leakage probability when information is spread to the friends of target users' friends. The Privacy leakage probability in information spreading paths comprises Individual Privacy Leakage Probability (IPLP) and Relationship Privacy Leakage Probability (RPLP). IPLP is calculated based on individuals' Privacy protection awareness and the trust of protecting others' Privacy, while RPLP is derived from relationship strength estimation. Experiments show that the security risk estimation framework can assist users to find vulnerable friends by calculating the average and the maximum Privacy leakage probability in all information spreading paths of target user in social network. Besides, three unfriending strategies are applied to decrease risk of Privacy leakage and unfriending the maximum degree friend is optimal.
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security risk estimation of social network Privacy Issue
Proceedings of the 2017 the 7th International Conference on Communication and Network Security, 2017Co-Authors: Xueqin Zhang, Li ZhangAbstract:Users in social network are confronted with the risk of Privacy leakage while sharing information with friends whose Privacy protection awareness is poor. This paper proposes a security risk estimation framework of social network Privacy, aiming at quantifying Privacy leakage probability when information is spread to the friends of target users' friends. The Privacy leakage probability in information spreading paths comprises Individual Privacy Leakage Probability (IPLP) and Relationship Privacy Leakage Probability (RPLP). IPLP is calculated based on individuals' Privacy protection awareness and the trust of protecting others' Privacy, while RPLP is derived from relationship strength estimation. Experiments show that the security risk estimation framework can assist users to find vulnerable friends by calculating the average and the maximum Privacy leakage probability in all information spreading paths of target user in social network. Besides, three unfriending strategies are applied to decrease risk of Privacy leakage and unfriending the maximum degree friend is optimal.