NSF DSSP Workshop: Data Science for Secure and Privacy-Aware Big Data Management and Mining

在大数据时代，跨众多应用领域，大量数据被收集、集成和分析，以支持丰富的应用语义，并实现数据驱动的科学发现。虽然可以访问更多的数据会带来更多的分析、挖掘、学习和知识发现的机会，但与此同时，它也增加了安全漏洞和侵犯隐私的机会。从本质上讲，对手能够获得对应用程序或系统的更多见解，并配备更多的背景知识，该奖项支持组织一个研讨会，将来自数据科学和安全/隐私不同子领域的研究人员和从业人员聚集在一起，讨论数据科学和安全/隐私的愿景，挑战，以及在大数据管理和挖掘中保护安全和隐私的机会。研讨会的议程通过确定与安全和隐私有关的数据科学研究的挑战，对科学界和整个社会产生了更广泛的影响，通过跨学科的数据科学方法，促进安全和隐私意识数据管理和挖掘的愿景。更具体地说，研讨会增加了几个相关社区之间的互动，包括密码学，数据库和数据管理，挖掘和学习，系统，社会和行为科学以及应用安全。

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.