Big Data is characterized by the integration of a significant amount of data, of varying modalities or types, at a pace that cannot be handled by traditional data management systems. This has sparked innovation in the collection, processing and storage of this data. The analytic systems built to leverage Big Data have yielded (and hold even greater promise to uncover) remarkable insights that enable a host of new applications that were not thought possible prior to the era of Big Data.

However, with this capacity to contribute to and benefit the greater good comes the responsibility to protect the subjects referenced in the data sets. In this context, the old adage is correct - “With great power, comes great responsibility”. Ultimately, the data subjects own the data and they stand to suffer most significantly from the data’s compromise.

Thus, there needs to be advances in techniques for 1) ingesting Big Data in a secure and privacy-preserving, 2) performing Big Data analysis in a secure environment and in a privacy-preserving manner, and 3) storing and enforcing retention policy securely (and in private modes) for Big Data systems. If these solutions are not in place, then the willingness of people to contribute their data to be included in a Big Data system decreases. Additionally, Big Data professionals need to perform risk analyses, as they relate to security and privacy, to get a realistic view of the safety of the landscape.

There is a lot of work to be done in this emerging field. This workshop is a venue for researchers and practitioners to come together and tackle them in a supportive and stimulating environment.

This workshop will include (but is not limited to):

Security and/or Privacy Technologies for collecting, processing and storing Big Data.
Theoretical Foundations of Security and/or Privacy of Big Data software, protocols, systems and infrastructure.
Security and/or Privacy analysis of Big Data software, protocols, systems and infrastructure.
Big Data Forensic Analysis.
Trust Management theory and software for Big Data.
Accountability Theory and Technologies for Big Data.
Integrating Legal considerations in Big Data Security and Privacy solutions and technologies.
User studies on Security and Privacy for Big Data software, protocols, systems and infrastructure.
Usable Security and Privacy for Big Data software, protocols, systems and infrastructure.

For more information, contact Tyrone Grandison (tgrandison@proficiencylabs.com) or Raquel Hill (ralhill@indiana.edu)