Balancing Privacy and Analysis in Data Sanitization

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CCR-0311671Balancing Privacy and Analysis in Data SanitizationMatt BishopWhen handling an intrusion, one organization may need to sharelogs and network traces with other organizations. However, thedata to be shared may contain sensitive information that thefirst organization does not wish to disclose to the other parties.The solution is to sanitize the data by removing information fromthe data. This protects the privacy of the organization. However,the data removed may be essential for the analysis of the data thatthe other organizations must perform. This research explores thetension between privacy and security analysis. The goals of thisresearch are to: (1) develop a sanitizing language to describethe requirements for privacy and security analysis in such a waythat the requirements can be automatically checked for inconsistencies;(2) determine the conditions under which "perfect sanitization"can occur, if any; and (3) examine the problem of sanitizinga dynamic data set that changes as the sanitization proceeds.This will involve developing and testing the sanitizationlanguage on both data extracted from a network, and on filescontaining student grades. The former will provide examples of bothstatic and dynamic data. Perfect sanitization will be studied bytranslating the sanitization process into functions and analyzingtheir ranges and domains.The significance of this work is in the balance of privacy and security.Previous work focuses on sanitizing data in an ad hoc manner, ratherthan analyzing the balance between privacy and security and allowingthe sanitizers to choose among particular requirements when the needsof privacy and security analysis conflict. Its impact is that ifsuccessful, the results can be used in a wide variety of fields inwhich privacy and analysis (not just security analysis) mustbe balanced.