Privacy-Preserving and Action-Event Sequence Data Mining and Advanced Data Structures for Efficient Heuristic Search

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Recent advances in machine learning and data mining techniques present the opportunity of applying such techniques to governmental and institutional databases. One current challenge is to learn about the general behavior of individuals so that appropriate actions can be taken for specific individuals by governments and institutions while protecting the privacy of all. One purpose of this research project is to address this challenge for data collected as sequences of events concerning individuals or entities. For Project 1, I will devise and evaluate methods for anonymization for privacy preserving data mining. Secondly, I will  devise and evaluate techniques to find high-utility actions to choose for particular event sequences based on the known utilities of diverse anonymized sequences. For Project 2, I will continue research on a promising thrust towards devising new data structures for efficient heuristic search.