Assessing China's Cybersecurity Law

Ai-min, QI; Shao, Guosong; Zheng, Wentong

doi:10.1016/j.clsr.2018.08.007

Cited by 34 publications

(16 citation statements)

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“…For example, Articles 13-15 in the European Union's General Data Protection Regulation (GDPR) [207] require algorithms that make decisions that âĂĲsignificantly affect" individuals to provide explanations ("right to explanation") by May 28, 2018. Similar laws have also been established in countries such as the United States [48] and China [166].…”

Section: Model Transparency Interpretability and Accountabilitymentioning

confidence: 81%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

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Section: Model Transparency Interpretability and Accountabilitymentioning

confidence: 81%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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“…For example, Articles 13-15 in the European Union's General Data Protection Regulation [177] require algorithms that make decisions that "significantly affect" individuals to provide explanations ("right to explanation") by May 28, 2018. The United States [38] and China [140] have also established similar laws.…”

Section: Model Transparency Interpretability and Accountabilitymentioning

confidence: 99%

Event Prediction in the Big Data Era

Zhao

2021

ACM Comput. Surv.

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.

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“…Internet has been the biggest change in recent history and poses a threat to privacy, and hence there has been a trove of researches on it in the legal (e.g. Lee, 2018;Qi et al, 2018;Greenleaf and Livingston, 2016) and information system (IS) (e.g. Kayworth et al, 2005;Lebek et al, 2013) disciplines.…”

Section: Legal Perspective In Privacy-related Researchmentioning

confidence: 99%

Privacy-deprived e-commerce: the efficacy of consumer privacy policies on China's e-commerce websites from a legal perspective

Zhu

Srivastava²,

Sutanto

2020

ITP

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PurposeDespite grave public concerns over information privacy and ongoing academic explorations of privacy policy, there is a general lack of understanding toward this issue in the legal context in China, the largest e-commerce market in the world. Departing from the extant literature of general discussion in nature, the authors undertook an exploratory study on the efficacy of e-commerce websites' privacy policies in China from the legal perspective.Design/methodology/approachThe authors drew on a qualitative grounded theory approach to identify selective codes relating to the focal issue and established a theoretical framework therefrom. The authors then conducted theoretical integration by linking them to the Theory of Development Blocks and the System Justification Theory.FindingsThe research identifies a general distrust of Chinese consumers toward privacy policies and highlights that despite their growing concerns about privacy, the privacy policies are largely ineffective in reflecting legal enforcement, changing their perceptions or influencing purchase behaviors. It also reveals that the current Chinese legislation is unable to fully render consumers' confidence in e-commerce websites' privacy policies effectiveness and privacy protection due to its limited recognition and influences among them.Originality/valueThe research has multiple ramifications. The authors empirically confirmed a mismatch between customers' perception of privacy policies and their actual behaviors and then theoretically explained the seemingly conflicting scenario in the context of development block of legal enforcement and system justification. The authors theorized the absence of the legal enforcement in privacy policies to supplement the legal perspective to the literature. The research further leads us to suggest that the time has come to update and strongly enforce privacy regulation in China to fuel the further development of e-commerce sector in practice.

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Assessing China's Cybersecurity Law

Cited by 34 publications

References 0 publications

Event Prediction in Big Data Era: A Systematic Survey

Event Prediction in Big Data Era: A Systematic Survey

Event Prediction in the Big Data Era

Privacy-deprived e-commerce: the efficacy of consumer privacy policies on China's e-commerce websites from a legal perspective

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