Federated RDF systems allow users to retrieve data from multiple independent sources without needing to have all the data in the same triple store. The performance of these systems can be poor for large and geographically distributed RDF data where network transfer costs are high. This article introduces CBTP-OL and CBTP-Nhop, two novel join algorithms that take advantage of network topology to decrease the cost of processing Basic Graph Pattern (BGP) SPARQL queries in a geographically distributed environment. Federation members are grouped in clusters, based on the network communication cost between the members, and the bulk of the join processing is pushed to the clusters. Our CBTP-OL and CBTL-Nhop algorithms use anoverlap listand, respectively, anN-hop overlap list, to efficiently compute join results from triples in different clusters. We implement our algorithms in the OpenRDF Sesame federated framework and use Apache Rya triple store instances as federation members. Experimental evaluation results show the advantages of our approach over existing techniques.

Contact tracing is the process of identifying people who came into contact with an infected person (“case”) and collecting information about these contacts. Contact tracing is an essential part of public health infrastructure and slows down the spread of infectious diseases. Existing contact tracing methods are extremely time and labor intensive due to their reliance on manually interviewing cases, contacts, and locations visited by cases. Additionally, complex privacy regulations mean that contact tracers must be extensively trained to avoid improper data sharing. App-based contact tracing, a proposed solution to these problems, has not been widely adopted by the general public due to privacy and security concerns. We develop a secure, semantically rich framework for automating the contact tracing process. This framework includes a novel, flexible ontology for contact tracing and is based on a semi-federated data-as-a-service architecture that automates contact tracing operations. Our framework supports security and privacy through situation-aware access control, where distributed query rewriting and semantic reasoning are used to automatically add situation based constraints to protect data. In this paper, we present our framework along with the validation of our system via common use cases extracted from CDC guidelines on COVID-19 contact tracing.

Intrusion Detection Systems are commonly used by organizations to monitor network traffic and detect attacks or suspicious behaviours. However, many attacks occur across organizations and are often difficult to detect using any single IDS. Collaborative Intrusion Detection Systems could lead to more accurate prediction and detection of cyber threats as well as a reduction of security administrators’ workload as similar threats from different places can be merged. However, most organizations are unwilling to disclose sensitive information about their internal network topology and traffic, lending these systems unusable. Existing solutions using homomorphic encryption and secure multi-party computation are often expensive. In this paper, we propose efficient and privacy preserving techniques to correlate alerts generated at different organizations. We propose skPrototypes, a distributed clustering algorithm for horizontally partitioned mixed data using additive secret sharing. This algorithm can be used to create a privacy preserving, collaborative intrusion detection system. We also propose dpkPrototypes which uses differential privacy on categorical attributes and is more efficient than skPrototypes for categorical attributes with many distinct values. Theoretical and experimental results validate the effectiveness of our algorithms.

Users often need to integrate large amounts of RDF data from multiple sources. Although there has been a lot of work on data cleaning and integration for structured data, relatively little work has been done for RDF data. We consider two different approaches to data integration: 1) a traditional data warehouse approach where relevant RDF data is extracted from different sources and then integrated in a data warehouse; 2) a virtual integration approach where RDF data still resides at each source and data integration happens when the data is queried, through a mediator that coordinates with wrappers at each source. It is often unclear how to choose the appropriate approach given an application scenario. This paper proposes RDFINT, a benchmark to compare these two approaches for integrating RDF data. We describe typical data integration operations, metrics that can be used to compare these two approaches, and factors that affect these metrics. We also report preliminary results of an implementation of these two approaches using the Apache Jena Fuseki framework.

Due to the high priority of cyber-security education, the United States Naval Academy rapidly developed and implemented a new cyber-security course that is required for all of its first-year students. During the fall semester in 2011, half of the incoming class (about 600 students) took the course through a total of 31 sections offered by 16 instructors from a variety of disciplines and backgrounds. In the following spring semester, the remaining half of the first-year students will take the course. This paper explains the motivation that instigated and drove course development, the curriculum, teaching mechanics implemented, personnel required, as well as challenges and lessons learned from the first offering of the course. The information contained in this paper will be useful to those thinking of implementing a technical course required of all students at the same level in an institution (in our case first-year students) and particularly those interested in implementing such a course in cyber security.