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2018-02-06
MüUller, W., Kuwertz, A., Mühlenberg, D., Sander, J..  2017.  Semantic Information Fusion to Enhance Situational Awareness in Surveillance Scenarios. 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). :397–402.

In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.

2017-12-20
Dong, B., Wang, H.(.  2017.  EARRING: Efficient Authentication of Outsourced Record Matching. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :225–234.

Cloud computing enables the outsourcing of big data analytics, where a third-party server is responsible for data management and processing. In this paper, we consider the outsourcing model in which a third-party server provides record matching as a service. In particular, given a target record, the service provider returns all records from the outsourced dataset that match the target according to specific distance metrics. Identifying matching records in databases plays an important role in information integration and entity resolution. A major security concern of this outsourcing paradigm is whether the service provider returns the correct record matching results. To solve the problem, we design EARRING, an Efficient Authentication of outsouRced Record matchING framework. EARRING requires the service provider to construct the verification object (VO) of the record matching results. From the VO, the client is able to catch any incorrect result with cheap computational cost. Experiment results on real-world datasets demonstrate the efficiency of EARRING.