An Integrated Knowledge Graph to automate Cloud Data Compliance

to address data protection concerns, authorities and standards bodies worldwide have released a plethora of regulations, guidelines, and software controls to be applied to Cloud data. As a result, service providers maintaining their end-user’s private attributes have seen a surge in compliance requirements. Since most of these regulations are not available in a machine-processable format, it requires significant manual effort to adhere to them. Often many of the laws have overlapping rules, but as they are not referencing each other, providers must duplicate efforts to comply with each regulation. We have done a detailed study of all the data protection regulations that apply to Cloud data. We have developed an integrated, semantically rich knowledge graph that captures these various data compliance regulations. It includes the data threats and security controls that are needed to mitigate the risks. In this paper, we …

For full paper please refer to the below link

https://ieeexplore.ieee.org/abstract/document/9139461

Developing Machine Learning based Predictive Models for Smart Policing

Crimes are problematic where normal social issues are confronted and influence personal satisfaction, financial development, and quality-of-life of a region. There has been a surge in the crime rate over the past couple of years. To reduce the offense rate, law enforcement needs to embrace innovative preventive technological measures. Accurate crime forecasts help to decrease the crime rate. However, predicting criminal activities is difficult due to the high complexity associated with modeling numerous intricate elements. In this work, we employ statistical analysis methods and machine learning models for predicting different types of crimes in New York City, based on 2018 crime datasets. We combine weather, and its temporal attributes like cloud cover, lighting and time of day to identify relevance to crime data. We note that weather-related attributes play a negligible role in crime forecasting. We have evaluated …

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https://ieeexplore.ieee.org/abstract/document/8784006

An Integrated Knowledge Graph to Automate GDPR and PCI DSS Compliance

Big data analytics related to consumer behavior, market analysis, opinions, and recommendation often deal with end user's derived and inferred data, along with the observed data. To ensure consumer data protection, rules defined by the European Union's General Data Protection Regulation (EU GDPR) must be adhered to by every organization using Personally Identifiable Information (PII) data for Big Data analysis. Similarly, Payment Card Industry Data Security Standard (PCI DSS) has policy guidelines specifically for organizations handling consumer's payment card data. Both data regulation policies are currently available only in textual format and require significant manual effort to ensure their compliance. We have developed an integrated, semantically rich Knowledge Graph (or Ontology) to represent the rules mandated by both PCI DSS and EU GDPR. In the Ontology

For full paper please refer to the below link

https://ieeexplore.ieee.org/abstract/document/9377864