This use case provides an overview of a Big Data application related to the shipping industry for which standards may emerge in the near future. Table 10: Mapping Cargo Shipping to the Reference Architecture NBDRA Security and Privacy Topic Use Case Mapping Component and Interfaces Data Provider End-point input validation Ensuring integrity of data collected from sensors → Application Real-time security monitoring Sensors can detect abnormal Provider temperature/environmental conditions for packages with special requirements. They can also detect leaks/radiation Data discovery and classification --- Secure data aggregation Securely aggregating data from sensors Application Privacy-preserving data analytics Sensor-collected data can be private and can Provider Data reveal information about the package and → Consumer The revealing of such information needs to preserve privacy Compliance with regulations --- Government access to data and The U.S. Department of Homeland Security freedom of expression concerns may monitor suspicious packages moving into/out of the country Data Provider Data-centric security such as --- ↔ Framework identity/policy-based encryption Provider Policy management for access Private, sensitive sensor data and package data control should only be available to authorized individuals. Third-party commercial offerings may implement low-level access to the data Computing on the encrypted data: See above section on “Transformation” homomorphic encryption Audits --- Framework Securing data storage and Logging sensor data is essential for tracking Provider transaction logs packages. Sensor data at rest should be kept in secure data stores Key management For encrypted data Security best practices for The diversity of sensor types and data types may data stores necessitate the use of non-relational data stores Security against DoS attacks --- Data provenance Metadata should be cryptographically attached to the collected data so that the integrity of origin and progress can be assured. Complete preservation of provenance will sometimes mandate a separate Big Data application Fabric Analytics for security intelligence Anomalies in sensor data can indicate tampering/fraudulent insertion of data traffic
This use case provides an overview of a Big Data application related to the shipping industry for which standards may emerge in the near future. Table 10: Mapping Cargo Shipping to the Reference Architecture NBDRA Component and Interfaces Security and Privacy Topic Use Case Mapping Data Provider → Application Provider End-point input validation Ensuring integrity of data collected from sensors Real-time security monitoring Sensors can detect abnormal temperature/environmental conditions for packages with special requirements. They can also detect leaks/radiation Data discovery and classification --- Secure data aggregation Securely aggregating data from sensors Application Provider → Data Consumer Privacy-preserving data analytics Sensor-collected data can be private and can reveal information about the package and The revealing of such information needs to preserve privacy Compliance with regulations --- Government access to data and freedom of expression concerns The U.S. Department of Homeland Security may monitor suspicious packages moving into/out of the country Data Provider ↔ Framework Provider Data-centric security such as identity/policy-based encryption --- Policy management for access control Private, sensitive sensor data and package data should only be available to authorized individuals. Third-party commercial offerings may implement low-level access to the data Computing on the encrypted data: homomorphic encryption See above section on “Transformation” Audits --- Framework Provider Securing data storage and transaction logs Logging sensor data is essential for tracking packages. Sensor data at rest should be kept in secure data stores Key management For encrypted data Security best practices for data stores The diversity of sensor types and data types may necessitate the use of non-relational data stores Security against DoS attacks --- Data provenance Metadata should be cryptographically attached to the collected data so that the integrity of origin and progress can be assured. Complete preservation of provenance will sometimes mandate a separate Big Data application Fabric Analytics for security intelligence Anomalies in sensor data can indicate tampering/fraudulent insertion of data traffic