Invited Talks
Sherif Sakr · 2017
The collection, storage, manipulation and retention of massive amounts of data have resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. For example, even if personally identifiable information is removed from the data, when data is combined with other data, an individual can be identified. This is essentially the inference and aggregation problem that data security researchers have been exploring for the past four decades. This problem is exacerbated with the management of big data as different sources of data now exist that are related to various individuals. While collecting massive amounts of data causes security and privacy concerns, big data analytics applications in cyber security is exploding. For example, an organization can outsource activities such as identity management, email filtering and intrusion detection to the cloud. This is because massive amounts of data are being collected for such applications and this data has to be analyzed. The question is, how can the developments in big data management and analytics techniques be used to solve security problems? These problems include malware detection, insider threat detection, and intrusion detection. To address the challenges of big data security and privacy as well as big data analytics for cyber security applications, we organized a workshop sponsored by the National Science Foundation in September 2014 and presented the results in 2015 at an inter-agency workshop in Washington DC. Since then several developments have been reported on big data security and privacy as well as on big data analytics of cyber security. This talk will summarize the findings of the workshop and discuss the developments and directions.