Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific research.However,publishing and pr...Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific research.However,publishing and processing big data poses a privacy concern related to protecting individuals’sensitive information while maintaining the usability of the published data.Several anonymization methods,such as slicing and merging,have been designed as solutions to the privacy concerns for publishing big data.However,the major drawback of merging and slicing is the random permutation procedure,which does not always guarantee complete protection against attribute or membership disclosure.Moreover,merging procedures may generatemany fake tuples,leading to a loss of data utility and subsequent erroneous knowledge extraction.This study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data utility.In particular,the proposed method distributes the data into horizontal and vertical partitions.The lower and upper protection levels are then used to identify the unique and identical attributes’values.The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks.The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.展开更多
In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of ...In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion.Employees,however,have been exposed to different security risks because of working from home.Moreover,the rapid global spread of COVID-19 has increased the volume of data generated from various sources.Working from home depends mainly on cloud computing(CC)applications that help employees to efficiently accomplish their tasks.The cloud computing environment(CCE)is an unsung hero in the COVID-19 pandemic crisis.It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data.Despite the increase in the use of CC applications,there is an ongoing research challenge in the domains of CCE concerning data,guaranteeing security,and the availability of CC applications.This paper,to the best of our knowledge,is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE.Additionally,this paper also highlights the security risks of working from home during the COVID-19 pandemic.展开更多
基金This work was supported by Postgraduate Research Grants Scheme(PGRS)with Grant No.PGRS190360.
文摘Publishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan,conduct,and assess scientific research.However,publishing and processing big data poses a privacy concern related to protecting individuals’sensitive information while maintaining the usability of the published data.Several anonymization methods,such as slicing and merging,have been designed as solutions to the privacy concerns for publishing big data.However,the major drawback of merging and slicing is the random permutation procedure,which does not always guarantee complete protection against attribute or membership disclosure.Moreover,merging procedures may generatemany fake tuples,leading to a loss of data utility and subsequent erroneous knowledge extraction.This study therefore proposes a slicingbased enhanced method for privacy-preserving big data publishing while maintaining the data utility.In particular,the proposed method distributes the data into horizontal and vertical partitions.The lower and upper protection levels are then used to identify the unique and identical attributes’values.The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks.The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.
文摘In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion.Employees,however,have been exposed to different security risks because of working from home.Moreover,the rapid global spread of COVID-19 has increased the volume of data generated from various sources.Working from home depends mainly on cloud computing(CC)applications that help employees to efficiently accomplish their tasks.The cloud computing environment(CCE)is an unsung hero in the COVID-19 pandemic crisis.It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data.Despite the increase in the use of CC applications,there is an ongoing research challenge in the domains of CCE concerning data,guaranteeing security,and the availability of CC applications.This paper,to the best of our knowledge,is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE.Additionally,this paper also highlights the security risks of working from home during the COVID-19 pandemic.
