Computing on Encrypted Data Searching and Reporting: Cryptographic protocols that support searching and reporting on encrypted data – any information about the plaintext not deducible from the search criteria are guaranteed to be hidden. Fully Homomorphic Encryption: Cryptographic protocols that support operations on the underlying plaintext of an encryption – any information about the plaintext is guaranteed to be hidden. Secure Data Aggregation: Aggregating data without compromising privacy. Key Management Provenance End-point Input Validation: Mechanism to validate whether input data is coming from an authenticated source, such as digital signatures. Syntactic: Validation at a syntactic level. Semantic: Semantic validation is an important concern. Generally, semantic validation would validate typical business rules such as a due date. This capability can extend to the relationship of the particular file format and that the constructs which may be based on a standard can have a variance that may poise a denial of service in the form of a lockup of an application. This may also be observed even when using data translators which may not recognize the particular variant. Protocols and data formats may be altered by a vendor using reserved data field for example that will allow their products to have capabilities that differentiate their products from other products. This may also appears in differences in versions of systems for consumer devices, such as mobile devices such as cell phones. The semantic of a message and the data that is to be transported should be validated to assure at minimum conformant with standards. The use of digital signatures will be important to provide assurance that the data has been verified using a validator or data checker. This may be important to provide assurance that data from a sensor or from the data provider. This capability is important particularly if the data is to be Transformed or in the Curation of the data. If that data fails to meet the requirements it may be discarded and if that data continues to present a problem the source may be restricted in its ability to submit the data. These types of errors would be logged and prevented from being able to be disseminated to consumers. The Big Data system is a special case where use of digital signatures will be very important.
Disposition, migration, retention policies PII microdata as “hazardous” (Kum et al. 2013) De-identification and Anonymization Re-identification risk management End Point Validation Digital Rights Management Visibility and Infrastructure Management Threat and Vulnerability Management DoS-resistant cryptographic protocols Monitoring, Alerting As pointed out in the CIICF, Big Data affords new opportunities for large scale security intelligence, complex event fusion, analytics and monitoring. Mitigation Breach mitigation planning for Big Data may be qualitatively or quantitatively different. Configuration Management Configuration management is one aspect of preserving system and data integrity Patch Management Upgrades Logging Big Data must produce and manage more logs, of greater diversity and velocity. E.g., profiling and Statistical sampling on an ongoing basis may be required. Malware Surveillance and Remediation This is a well-understood domain, but Big Data can cross traditional system ownership boundaries. Review of Identify, Protect, Detect, Respond and Recover framework (NIST Oct 2013) may uncover planning unique to Big Data. Network Boundary control NEW: Establishes a data-agnostic connection for a secure channel. Shared services network architecture Ed Note: Akhil to expound. Zones/ cloud network design (including connectivity) Resilience, Redundancy and Recovery Resilience The security apparatus for a Big Data system may be comparatively fragile in comparison to other systems. Recovery Recovery for Big Data security failures may require considerable advance provisioning beyond that required for small data. Response planning and communications with users and may be on a similarly big scale. Risk and Accountability Accountability
Data Discovery and Classification Discovery / classification possible across media, populations, channels Secure Data Aggregation Vendor-supplied aggregation services – security practices opaque Transformation → Uses Privacy-preserving Data Analytics Aggregate reporting to content owners Compliance with Regulations PII disclosure issues abound Govt access to data and freedom of expression concerns Various issues, e.g, playing terrorist podcast, illegal playback Transformation ↔ Data Infrastructure Data Centric Security such as identity/policy-based encryption unknown Policy management for access control User, playback admin, library maintenance, auditor Computing on the encrypted data: searching/filtering/deduplicate/fully homomorphic encryption Unknown Audits Audit DRM usage for royalties Data Infrastructure Securing Data Storage and Transaction logs unknown Key Management unknown Security Best Practices for non-relational data stores unknown Security against DoS attacks N/A? Data Provenance Traceability to right entities to be preserved. (Add’l use case: Wikipedia privacy issues when distributing data to researchers) General Analytics for security intelligence Machine intelligence for unsanctioned use/access Event detection “Playback” granularity defined Forensics Subpoena of playback records in legal disputes Unmanned Military Vehicle Sensor Systems Unmanned vehicles (“drones”) and their onboard sensors (e.g., streamed video) can produce petabytes of data that must be stored in nonstandard formats. Refer to DISA large data object contract [11] for exabytes in DoD private cloud. Mapping to the Security Reference Architecture: