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In critical infrastructure (CI) sectors such as emergency management or healthcare, researchers can analyze and detect useful patterns in data and help emergency management personnel efficaciously allocate limited resources or detect epidemiology spread patterns. However, all of this data contains personally identifiable information (PII) that needs to be safeguarded for legal and ethical reasons. Traditional techniques for safeguarding, such as anonymization, have shown to be ineffective. Differential privacy is a technique that supports individual privacy while allowing the analysis of datasets for societal benefit. This paper motivates the use of differential privacy to answer a wide range of queries about CI data containing PII with better privacy guarantees than is possible with traditional techniques. Moreover, it introduces a new technique based on Multipleattribute Workload Partitioning, which does not depend on the nature of the underlying dataset and provides better protection for privacy than current differential privacy approaches.