The Experts below are selected from a list of 209916 Experts worldwide ranked by ideXlab platform
Zheng Huo - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preservation in Big Data From the Communication Perspective—A Survey
IEEE Communications Surveys & Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
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privacy preservation in big data from the Communication Perspective a survey
IEEE Communications Surveys and Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
Tao Wang - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preservation in Big Data From the Communication Perspective—A Survey
IEEE Communications Surveys & Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
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privacy preservation in big data from the Communication Perspective a survey
IEEE Communications Surveys and Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
Zhigao Zheng - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preservation in Big Data From the Communication Perspective—A Survey
IEEE Communications Surveys & Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
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privacy preservation in big data from the Communication Perspective a survey
IEEE Communications Surveys and Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
Shihong Yao - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preservation in Big Data From the Communication Perspective—A Survey
IEEE Communications Surveys & Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
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privacy preservation in big data from the Communication Perspective a survey
IEEE Communications Surveys and Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
Mubashir Husain Rehmani - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preservation in Big Data From the Communication Perspective—A Survey
IEEE Communications Surveys & Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
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privacy preservation in big data from the Communication Perspective a survey
IEEE Communications Surveys and Tutorials, 2019Co-Authors: Tao Wang, Zhigao Zheng, Mubashir Husain Rehmani, Shihong Yao, Zheng HuoAbstract:The advancement of data Communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the Communication Perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in Communication field.
