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Over the years social network data has been mined to predict individuals' traits such as intelligence and sexual orientation. While mining social network data can provide many beneficial services to the user such as personalized experiences, it can also harm the user when used in making critical decisions such as employment. In this work, we investigate the reliability of applying data mining techniques...
systems for providing data privacy, there is no general methodology for determining the extent to which these techniques, tools and systems reduce practical privacy risks. We need a comprehensive framework where the privacy and utility of multiple privacy-preserving techniques could be measured. This vision paper provides directions for designing such a framework.
This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe...
In our current work, we have proposed a multi-tiered ensemble based robust method to address all of the challenges of labeling instances in evolving data stream. Bottleneck of our current work is, it needs to build ADABOOST ensembles for each of the numeric features. This can face scalability issue as number of features can be very large at times in data stream. In this paper, we propose an intelligent...
The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two such well studied aspects are infinite length and concept-drift. Since a data stream may be considered a continuous process, which is theoretically infinite in length, it is impractical to store and use all the historical data for...
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