Noise Infusion as a Confidentiality Protection Measure for Graph-Based Statistics

We use the bipartite graph representation of longitudinally linked employer-employee data, and the associated projections onto the employer and employee nodes, respectively, to characterize the set of potential statistical summaries that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightforward extension of the dynamic noise-infusion method used in the U.S. Census Bureau’s Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs

Dynamic Graphs on the GPU

We present a fast dynamic graph data structure for the GPU. Our dynamic graph structure uses one hash table per vertex to store adjacency lists and achieves 3.4–14.8x faster insertion rates over the state of the art across a diverse set of large datasets, as well as deletion speedups up to 7.8x. The data structure supports queries and dynamic updates through both edge and vertex insertion and deletion. In addition, we define a comprehensive evaluation strategy based on operations, workloads, and applications that we believe better characterize and evaluate dynamic graph data structures

Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty about the attacker's behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.Comment: 14 pages, 15 figure

Design of Experiments Approach for Statistical Classification of Stereolithography Manufacturing Build Parameters: Effects of Build Orientation on Mechanical Properties for ASTM D-638 Type I Tensile Test Specimens of DSM Somos® 11120 Resin

A statistical design of experiments (DOE) approach was used to determine if specific build orientation parameters impacted mechanical strength of fabricated parts. A single platform (10- inch by 10 inch cross-section) on the 3D Systems Viper si2 machine was designed to hold 18, ASTM D-638 Type I samples built in six different orientations (called Location) with three samples built for each location. The DOE tested four factors: Location, Position, Axis, and Layout. Each sample within a Location was labeled as Positions 1, 2, or 3 depending on the distance from the center of the platform with Position 1 being the closest to the center. Samples were fabricated parallel with the x-axis, y-axis, or 45o to both axes (called Axis 1, 2, and 3, respectively) and were fabricated either flat or on an edge relative to the x-y plane (called Layout 1 and 2, respectively). The results from the statistical analyses showed that Axis, Location, and Position had no significant effect on UTS or E. However, Layout (or whether a sample was built flat or on an edge) was shown to have a statistically significant effect on UTS and E (at a 95% level of confidence). This result was not expected since a comparison of the average UTS for each Layout showed only a 1.2% difference (6966 psi versus 7050 psi for samples built flat and on an edge, respectively). Because of the small differences in means for UTS, the statistical differences between Layout most likely would not have been identified without performing the DOE. Furthermore, Layout was the only factor that tested different orientations of build layers (or layer-to-layer interfaces) with respect to the sample part, and thus, it appears that the orientation of the build layer with respect to the fabricated part has a significant effect on the resulting mechanical properties. This study represents one of many to follow that is using statistical analyses to identify and classify important fabrication parameters on mechanical properties for layer manufactured parts. Although stereolithography is the focus of this work, the techniques developed here can be applied to any layered manufacturing technology.Mechanical Engineerin

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.Comment: in 18th International Conference on Extending Database Technology (EDBT) (2015