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The growth of data mining has raised concerns among privacy advocates. Some of this is based on a misunderstanding of what data mining does. The previous chapters have shown how data mining concentrates on extraction of rules, patterns and other such summary knowledge from large data sets. This would not seem to inherently violate privacy, which is generally concerned with the release of...
A vast majority of the world's valuable data currently exists in relational databases and other legacy storage systems. In order for Semantic Web applications to access such legacy data without replication or synchronization of the same, the gap between the two needs to be bridged. Several efforts exist that publish relational data as Resource Description Framework (RDF) triples, however almost all...
Classification of nodes in a social network and its applications to security informatics have been extensively studied in the past. However, previous work generally does not consider the types of links (e.g., whether a person is friend or a close friend) that connect social networks members for classification purposes. Here, we propose modified Naive Bayes Classification schemes to make use of the...
Context-based anomaly tracking represents a new approach to security enhancement of communication streams. By creating a system that develops an understanding of normal and abnormal based on communication history, it is possible to detect fluctuations in an evolving social network. Although more research is necessary to overcome current obstacles, the combination of social network analysis and anomaly...
This paper describes our approach to assured information sharing. The research is being carried out under a MURI 9Multiuniversity Research Initiative) project funded by the air force office of scientific research (AFOSR). The main objective of our project is: define, design and develop an assured information sharing lifecycle (AISL) that realizes the DoD's information sharing value chain. In this...
Bioterrorism represents a serious threat to the security of civilian populations. The nature of an epidemic requires careful consideration of all possible vectors over which an infection can spread. Our work takes the SIR model and creates a detailed hybridization of existing simulations to allow a large search space to be explored. We then create a Stackelberg game to evaluate all possibilities with...
In recent years, anonymization methods have emerged as an important tool to preserve individual privacy when releasing privacy sensitive data sets. This interest in anonymization techniques has resulted in a plethora of methods for anonymizing data under different privacy and utility assumptions. At the same time, there has been little research addressing how to effectively use the anonymized data...
Privacy preserving data mining has been investigated extensively. The previous works mainly fall into two categories, perturbation and randomization based approaches and secure multi-party computation based approaches. The earlier perturbation and randomization approaches have a step to reconstruct the original data distribution. The new research in this area adopts different data distortion methods...
This paper describes the issues and challenges for real-time knowledge discovery and then discusses approaches and challenges for real-time data mining and stream mining. Our goal is to extract accurate information to support the emergency responder, the war fighter, as well as the intelligence analyst in a timely manner.
Bioterrorism represents a significant threat to society. The lack of successful attacks that have resulted in true epidemics have created a need for data that can be generated from existing known factors. We have taken the popular susceptible-infected-recovery model and created a hybridized model that balances the simplicity of the original with an approximation of what more complex agent-based models...
Real-world entities are not always represented by the same set of features in different data sets. Therefore matching and linking records corresponding to the same real-world entity distributed across these data sets is a challenging task. If the data sets contain private information, the problem becomes even harder due to privacy concerns. Existing solutions of this problem mostly follow two approaches:...
To support large-scale biomedical research projects, organizations need to share person-specific genomic sequences without violating the privacy of their data subjects. In the past, organizations protected subjects' identities by removing identifiers, such as name and social security number; however, recent investigations illustrate that deidentified genomic data can be ldquoreidentifiedrdquo to named...
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