Secure Statistical Analysis of Distributed Databases

A continuing need in the contexts of homeland security, national defense and counterterrorism is for statistical analyses that “integrate” data stored in multiple, distributed databases. There is some belief, for example, that integration of data from flight schools, airlines, credit card issuers, immigration records and other sources might have prevented the terrorist attacks of September 11, 2001, or might be able to prevent recurrences. In addition to significant technical obstacles, not the least of which is poor data quality [32, 31], proposals for large-scale integration of multiple databases have engendered significant public opposition. Indeed, the outcry has been so strong that some plans have been modified or even abandoned. The political opposition to “mining” distributed databases centers on deep, if not entirely precise, concerns about the privacy of database subjects and, to a lesser extent, database owners. The latter is an issue, for example, for databases of credit card transactions or airline ticket purchases. Integrating the data without protecting ownership could be problematic for all parties: the companies would be revealing who their customers are, and where a person is a customer would also be revealed. For many analyses, however, it is not necessary actually to integrate the data. Instead, as we show in this paper, using techniques from computer science known generically as secure multi-party computation, the database holders can share analysis-specific sufficient statistics anonymously, but in a way that the desired analysis can be performed in a principled manner. If the sole concern is protecting the source rather than the content of data elements, it is even possible to share the data themselves, in which case any analysis can be performed. The same need arises in non-security settings as well, especially scientific and policy investigations. For example, a regression analysis on integrated state databases about factors influencing student performance