Patient health records possess a great deal of information that would be useful in medical research, but access to these data is impossible or severely limited because of the private nature of most personal health records. Anonymization strategies, to be effective, must usually go much further than simply omitting explicit identifiers because even statistics computed from groups of records can often be leveraged by hackers to re-identify individuals. Methods of balancing the informativeness of data for research with the information loss required to minimize disclosure risk are needed before these private data can be widely released to researchers who can use them to improve medical knowledge and public health. We have developed an integrated software system that provides solutions for anonymizing data based on interval generalization, controlling data utility, and performing statistical analyses and making inferences using interval statistics.
Operating System: Cloud based web application