In the era of big data, across numerous application domains, large amounts of data are being collected, integrated, and analyzed to support rich application semantics and enable data-driven science discovery. While having the access to more data leads to more opportunities for analytics, mining, learning, and knowledge discovery, at the same time, it also increases the chance of security breach and privacy infringement. In essence, adversaries are able to gain more insights into an application or system, and equipped with more background knowledge, when more (relevant) data are made available to him/her. This award supports the organization of a workshop to bring together researchers and practitioners from different sub-fields of data science and security/privacy to discuss the vision, challenges, and opportunities for the protection of security and privacy in big data management and mining. The agenda of the workshop has broader impacts on the scientific communities and the society at large by identifying the challenges for data science research related to security and privacy, and foster the vision for secure and privacy-aware data management and mining through an interdisciplinary data science approach. More specifically, the workshop increases the interactions among several related communities, including cryptography, database and data management, mining and learning, systems, social and behavior sciences, and applied security.
NSF DSSP Workshop: Data Science for Secure and Privacy-Aware Big Data Management and Mining