ABSTRACT

Large-scale data management systems rely more and more on cloud storage, where the need for efficient search capabilities clashes with the need for data privacy. Searchable encryption is a tool for supporting queries to en- crypted databases which provides various levels of efficiency, functionality, and security.

The provenance captured from different layers of abstraction (workflow/process/OS) provides the highest benefit when integrated through a unified provenance framework. To build such a framework, a comprehensive provenance model able to represent the provenance of data objects with various semantics and granularity is the first step. In this poster we present a provenance model able to represent the provenance of any data object captured at any abstraction layer and present an abstract schema of the model.

This poster will present secure network provenance (SNP), a novel capability that enables networked systems to explain to their operators why they are in a certain state – e.g., why a suspicious routing table entry is present on a certain router, or where a given cache entry originated. SNP provides network forensics capabilities by permitting operators to track down faulty or misbehaving nodes, and to assess the damage such nodes may have caused to the rest of the system.

 

ABSTRACT The PIs propose to explore the influence that offline cues and stimuli, indicating the presence of other human beings in the physical world, and often processed unconsciously by our brains, can have over security and privacy behavior in cyberspace. The PIs’ goal is to address the question: are there evolutionary roots for privacy and information security concerns?

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ABSTRACT